System and method to manage diabetes based on hypoglycemic risk

ABSTRACT

A system and method provides a glucose report for determining glycemic risk based on an ambulatory glucose profile of glucose data over a time period, a glucose control assessment based on median and variability of glucose, and indicators of high glucose variability. Time of day periods are shown at which glucose levels can be seen. A median glucose goal and a low glucose line provide coupled with glucose variability provide a view into effects that raising or lowering the median goal would have. Likelihood of low glucose, median glucose compared to goal, and variability of glucose below median provide probabilities based on glucose data. Patterns can be seen and provide guidance for treatment.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/122,970, filed Dec. 15, 2020, which is a continuation of U.S. patentapplication Ser. No. 16/005,234, filed Jun. 11, 2018, which is acontinuation of U.S. patent application Ser. No. 14/214,901, filed Mar.15, 2014, now U.S. Pat. No. 10,010,291, which claims the benefit of U.S.Provisional Application No. 61/922,765, filed Dec. 31, 2013 and of U.S.Provisional Application No. 61/799,139, filed Mar. 15, 2013, all ofwhich applications are incorporated herein by reference in theirentireties for all purposes.

BACKGROUND

Achieving euglycemia can be hampered by episodes of hypoglycemia andglucose variability which can now be tracked by continuous glucosemonitoring (“CGM”). CGM devices have been shown to be clinicallyaccurate in recording hypoglycemia, and can be used to assess diurnalpatterns of glycemia. However, a challenge inherent to analysis of thisinflux of data is to represent it in a clinically meaningful manner thatenables efficient clinical action. There is a need for glucose reportsthat can provide standardized, efficient output to effectively guidetherapeutic decision making. Key benefits of glucose reports include aconsistent view of glucose trends and patterns over the day, and showingthe detail that A1C cannot. The identification of patterns ofhypoglycemia and glucose variability can aid by guiding how aggressivelythe treatment can be safely adjusted.

Although present glucose reports have provided a way to analyze theinflux of data from CGM, decision-making based on those reports andanalyses can still be a challenge. Computerized algorithms have beendeveloped as a way to simplify and guide the decision-making process. Inhospital settings, computerized algorithms have been shown to improvepatient outcomes by maintaining tight glucose control without increasinghypoglycemic events. In a clinical setting, computerized algorithms havealso aided clinicians in correctly identifying glycemic patterns, makingtherapeutic decisions to address patterns, and teaching staff andpatients.

Hence those skilled in the art have identified a need for presentinglarge amounts of CGM data in a useful manner. A need has also beenrecognized for analyzing CGM data so that possible effects in treatmentchanges can be analyzed. Further, a need has been recognized for areport that provides an overview of the glucose history of a patient andhow effective the present treatment has been. Yet another need is for aglucose-based report that presents an overview of the patient's glucosehistory on an hourly basis annotated by certain periods of the day sothat decisions may be made about possible treatment modification. Theinvention fulfills these needs and others.

Abbreviations—As used herein, the following abbreviations stand for theindicated terms:

-   -   A1C=glycated hemoglobin    -   AGP=ambulatory glucose profile    -   AU70=area under 70 mg/dL (3.9 mmol/L)    -   CG=control grid    -   CGM=continuous glucose monitor    -   FOM=figure of merit    -   GCA=glucose control assessment    -   Gm=median glucose    -   Gv=glucose variability    -   ITS=insulin titration sensitivity    -   JDRF=a trademark of Juvenile Diabetes Research Foundation; i.e.,        JDRF International    -   LGA=low glucose allowance    -   LLG=likelihood of low glucose    -   MTT=margin to treat    -   SMBG=self-monitored blood glucose    -   TMS=therapy management system    -   TRP=treatment recommendation point

SUMMARY OF THE INVENTION

Briefly and in general terms, the present invention is directed to asystem and method to provide a glucose report based on large amounts ofglucose data, the report showing patterns and analyses of those patternsof the glucose history of a patient as a tool for treatmentconsiderations. In accordance with system aspects, there is provided asystem for determining glycemic risk based on analysis of glucose data,the system comprising a non-volatile memory in which is stored a glucosedata processing program configured to program a processor to analyzereceived glucose data and from the analysis, produce a display, an inputat which glucose data is received. a display on which glucose data andanalytics thereof may be visually presented, a processor connected withthe nonvolatile memory, the input, and the display, the processor beingconfigured to access the memory to load and run in the processor theprogram to analyze glucose data, wherein the processor is programmed toanalyze the received glucose data to determine an estimate of ahypoglycemia measure, further analyze the received glucose data todetermine a measure of a central tendency of glucose data median and ameasure of the spread of glucose data from the central tendency, controlthe display to visually present differences of glucose in comparison toa central tendency of glucose data, and control the display to visuallypresent a glucose control measure that includes an assessment of theglucose data in the categories of likelihood of low glucose, medianglucose, and variability of glucose below the median with visualindicators conveying high, moderate, and low about each category.

In accordance with more detailed aspects, the processor is programmed todetermine a glucose median as the central tendency. The processor isprogrammed to determine the variability of glucose data about thecentral tendency. The processor is programmed to control the display tovisually present percentiles of glucose data in comparison to a medianglucose level.

In accordance with method aspects, there is provided a method fordetermining glycemic risk based on analysis of glucose data, the methodcomprising storing in a non-volatile memory a glucose data processingprogram configured to program a processor to analyze received glucosedata and from the analysis, produce a display, receiving glucose data,accessing the non-volatile memory and running the glucose dataprocessing program, analyzing the received glucose data to determine anestimate of a hypoglycemia measure, analyzing the received glucose datato determine a central tendency of the data, analyzing the receivedglucose data to determine a spread of the data from the centraltendency, control a display to visually present differences of theglucose in comparison to the central tendency of the glucose data, andcontrolling a display to visually present a glucose control measure thatincludes an assessment of the glucose data in the categories oflikelihood of low glucose, median glucose, and variability of glucosebelow the median with visual indicators conveying high, moderate, andlow about each category.

In more detailed method aspects, the step of analyzing received glucosedata to determine a central tendency comprises determining the median ofthe data. The step of analyzing received glucose data to determine aspread of the glucose data comprises determining variability of glucosedata from the central tendency. The step of visually presentingdifferences comprises visually presenting percentiles of glucose incomparison to the central tendency.

In yet other aspects, the steps of visually presenting central tendencyand spread and differences comprise determining a median of the glucosedata, determining variability of the glucose data from the median, andvisually presenting differences of the glucose data from the median inpercentiles of glucose in comparison to the median.

The features and advantages of the invention will be more readilyunderstood from the following detailed description that should be readin conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an Ambulatory Glucose (“AGP”) plot and Glucose ControlAssessment and indicators;

FIG. 2A-2D show additional AGP plots and glucose control assessmentswith indicators for patient examples from JDRF-CGM trial with laboratoryA1C=7.6% to 7.7% measured at time of sensor wear;

FIG. 3. Criteria for determining low, moderate, and high indicators inthe Glucose control Assessment (GCA);

FIGS. 4A through 4D present further AGP plots and assessments, and FIG.4E is a representation of the summary statistics for central tendencyand variability of the glucose distributions for each time period foreach patient;

FIGS. 5A-5C show the likelihood of low glucose (“LLG”) performance atLow Glucose Allowance (LGA) settings of large (A), medium (B) and small(C);

FIG. 6 provides a Control Grid with High Risk Curve for the Medium LowGlucose Allowance setting, Moderate Risk Curve for daytime with 14 daysof CGM, Median Goal=154 mg/dL;

FIG. 7 shows an application of the data management in accordance withaspects of the invention to achieve glucose control;

FIG. 8 presents safety performance of LLG and P₁₀ methods: Rate ofincorrect “Green” indicators when AU70 exceeds the criteria;

FIG. 9 presents sensitivity performance of LLG and P₁₀ methods: Rate ofcorrect “Red” indicators when AU70 exceeds the criterion.

FIG. 10 presents a correlation among colors, meanings, and drawingssymbols.

FIG. 11 shows an Overlay of Self-care Behavior Patterns, 30-hour plotfor “Low overtreatment without rebound Low;”

FIG. 12 also shows an Overlay of Self-care Behavior Patterns, relativeto kernel start time for “Los overtreatment without rebound Low;”

FIG. 13 shows a daily glucose profile example in which CGM data isshown;

FIG. 14 shows a daily glucose profile example having CGM data withpatterns overlayed;

FIG. 15 shows a another daily glucose profile example having CGM datawith patterns overlayed;

FIG. 16 shows a another daily glucose profile example having CGM datawith patterns overlayed;

FIG. 17 shows yet another daily glucose profile example having CGM datawith patterns overlayed;

FIG. 18 shows even another daily glucose profile example having CGM datawith patterns overlayed;

FIG. 19 shows a glucose control grid in which a patient's glucose medianand variability are plotted;

FIG. 20 shows a glucose metrics map to therapy recommendations;

FIG. 21 shows a treatment recommendation algorithm functional flow;

FIG. 22 shows a Glucose Control Grid modified from FIG. 19 with avariability line includes;

FIG. 23 shows an option treatment recommendation algorithm functionalflow;

FIG. 24 presents recommendations tailored to current treatment;

FIG. 25 shows a block diagram of threshold-based episode detectionalgorithm;

FIG. 26 shows a hypoglycemic episode example;

FIG. 27 shows a hyperglycemic episode example;

FIG. 28 shows a block diagram of change-based episode detectionalgorithm;

FIG. 29 shows a glucose rise episode detection example;

FIG. 30 shows a glucose fall episode detection example;

FIG. 31 is an example of a glucose control chart showing the states ofsixty-six patients with diabetes mellitus;

FIGS. 32A and 32B show an association between HbA1c and risk ofretinopathy and HbA1c and risk of severe hypoglycemia;

FIG. 33 shows an example of more than one clinical risk overlaid on aglucose control chart;

FIGS. 34A and 34B show the time relationship between glucose and13-hydroxybutyrate;

FIG. 35 shows a control grid of a patient's current glycemia state witha “Hypo Risk Line” with at least 95% certainty of no hypoglycemia risk;

FIG. 36 shows the sign of the MTT value being positive or negative,depending on patient data relative to hypo risk line;

FIG. 37 presents a one-step projection of a straight line fit based onpast data;

FIG. 38 is an illustration of the ADA/EASD consensus guideline;

FIG. 39 shows a determination of MTT taking into account both medianglucose and glucose variability and vector adaptation;

FIG. 40 is a scatter plot of FOM vs. south40 and median;

FIG. 41 is a scatter plot of FOM/2 vs. normalized south40;

FIG. 42 is a scatter plot of error using FOM/2=south40/√N;

FIG. 43 is a scatter plot using least squares fit;

FIG. 44 is an error (with LS fit) histogram showing an error histogramusing LS fit vs. normalized south 40;

FIG. 45 is a scatter plot of test set data showing error usingFOM/2=south40/√N;

FIG. 46 is a scatter plot of test set data showing error using leastsquares fit;

FIG. 47 is an error (with least squares fit) histogram of test set datashowing a test set error histogram using a least squares fit vs. anormalized south40;

FIG. 48 is a plot of glucose data can be modeled as a distribution;

FIGS. 49A, 40B, 49C, 49D, 49E, and 49F are plots of glycemia risk;

FIG. 50 is a grid with zones identified;

FIGS. 51A and 51B show the control grid;

FIGS. 52A and 52B show gamma distributions of glucose over time;

FIGS. 53A and 53B show two or more boundaries associated with a measure;

FIG. 54 is a graph of guidance zones;

FIGS. 55A and 55B show an embodiment of guidance output; and

FIG. 56 is a block diagram of a system usable to embody inventiveconcepts disclosed and claimed herein, including a processor, acontinuous glucose monitor, a memory, display, printer, and remoteconnections to servers.

DETAILED DESCRIPTION OF EMBODIMENTS

Referring now in more detail to the exemplary drawings for purposes ofillustrating embodiments of the invention, wherein like referencenumerals designate corresponding or like elements among the severalviews, there is shown in FIG. 1 a glucose-based report, referred toherein as the Insights report 69, having the three components of anAmbulatory Glucose Profile (“AGP”) plot 70, a Glucose Control Assessment(“GCA”) 72, and indicators for high glucose variability 74. Theindicators may be in color, preferably green, yellow, and red, wheregreen indicates a “low” level, yellow indicates a “moderate” level, andred indicates a “high” level of variability. In the black and whitefigures of FIGS. 1, 2A-2D, and 4A-4D, the levels are shown by circleswith slashes or the letters “OK.” Translation of the circles to color ispresented in FIG. 10 for one embodiment. In the case of the indicatorsfor Variability Below Median 74, the indicators are linked to the“variability below median” cells when the cells indicate a “HIGH” level.A box appears around the indicators and the HIGH level cells as shown inFIGS. 1 and 2B as examples. When none of the cells of the “variabilitybelow median” row indicate “HIGH,” the indicator box does not appear, asseen in FIGS. 2A, 4A, and 4C. In place of a box, a common color may beused. That is, the same fill color may be used for the cells of theVariability Below Median row indicating HIGH as the fill color for theindicators box (see FIG. 1). Other approaches may also be used.

In particular, a mathematically-based system and method has been usedthat exploits the relationship between glucose median, glucosevariability, and hypoglycemic risk to prepare a report, and can beimplemented in computer software. From this relationship, the glucosepattern report referred to as the “Insights” report 69 is produced.Examining the AGP 70, the GCA 72, and the indicators 74 provides a goodreference for the decision-making process in treatment.

The Insights report 69 is made up of the three primary componentsmentioned above; i.e., the Ambulatory Glucose Profile (“AGP”) plot 70,the Glucose Control Assessment (“GCA”) 72, and the indicators for highglucose variability 74, and is divided into time-of-day periods in FIG.1, and can be adjusted according to a person's typical routine. Thevariety of Insights reports for similar people with the same A1C isshown in FIGS. 2A-2D thus demonstrating the large increase in data andanalysis presented about each of those people by the Insights report.The AGP graph 70 displays the hourly 10^(th) (76), 25^(th) (78), 50th(median) (80), 75^(th) (82), and 90th (84) percentiles of glucosereadings, presented over the “typical” day based on all days within theselected timeframe. It will be noted that the AGP plot includes twohorizontal lines. These are a “median goal” line of 154 in thisembodiment, and a low glucose line of 70.

The first GCA 72 measure, “Likelihood of Low Glucose” (“LLG”) 86, is theprobability that low glucose values have exceeded an allowable,user-defined threshold. The second measure, “Median Glucose (Compared toGoal)” 88, is an indication of when the median glucose has exceeded theindividual's Median Goal setting. The third measure, “Variability belowMedian (Median to 10^(th) Percentile)” (90), is a measure of the spreadof glucose data below the median. It is calculated as the differencebetween the 50th and 10th percentile glucose readings for the timeperiod. It is important to note that when variability below the medianis high, it is difficult to achieve the median goal without increasingthe Likelihood of Low Glucose (86). Therefore, factors causing theelevated glucose variability must be addressed before insulin doses areincreased, otherwise there would be an increased risk for low glucose.The Insights report 69 also outlines factors that could contribute toHIGH variability below the median including “Erratic diet,” “Incorrector missed medication,” “Alcohol consumption,” “Variations in activitylevel,” or “Illness,” and that need to be reviewed and addressed by thehealth care professional in his/her counseling of the patient. The GCAindicators are low, moderate, or high based on the criteria 92 shown inFIG. 3.

The Median Goal parameter (FIG. 3) sets the glucose level for whichMedian Glucose is reported as Low, Moderate, or High. The median glucoseprovides a useful measure because of its strong correlation with A1C.The overall glycemic management goal is to reduce median glucose levelsbelow the goal while minimizing the LLG, as this should result in A1Cand hypoglycemia exposure goals being met.

The Low Glucose Allowance parameter (FIG. 3) sets the threshold forwhich LLG is reported as Low, Moderate, or High. The setting options areSmall, Medium, or Large. Increasing this parameter allows more glucosereadings below 70 mg/dL (3.9 mmol/L) before causing the LLG to go fromLow to Moderate or High. The allowance was based on both the frequencyand value of low readings. The Insights report 69 is designed to allowthe clinician to adjust the allowance setting according to the clinicalscenario. For example, the allowance may be decreased for someone who iselderly or has hypoglycemia unawareness. Alternatively, the allowancemay be increased for a pregnant woman trying to maintain tight glucosecontrol.

In addition, the times of Daily Events (FIG. 1, area of AGP 70) definesthe periods during the day used to analyze the GCA 72. The user can setthe typical times for Breakfast, Lunch, Dinner, (apple icon) and Bedtime(person in bed icon). These times correspond to daily events that areclinically relevant to diabetes patients whose insulin therapy isrelated to eating and sleeping events. The result is three daytimeperiods and two overnight periods, with default time boundaries of 3 am,8 am, 12 pm, 6 pm and 10 μm. Therefore, a total of fifteen indicatorsare displayed in the GCA 72 to support review of the AGP 70.

The supporting role that the GCA 72 provides is shown for four patients,using data publicly-available from the JDRF Continuous GlucoseMonitoring Clinical Trial (JDRF-CGM trial) dataset in FIGS. 4A-4D. Thefirst patient, who is shown in FIG. 4A, had minimal likelihood of lowglucose across the day, as the median and 10^(th) percentile line weresubstantially elevated relative to the median goal (Median Goal (154))and the hypoglycemic boundary (Low Threshold (70), respectively. In thiscase, the GCA conformed to the visual interpretation of the AGP 70,indicating that glucose levels could be lowered safely with little riskof inducing hypoglycemia.

The second AGP 100 shown in FIG. 4B shows substantial hypoglycemiaacross the day, as the 10th percentile 76 was nearly always below 70mg/dL. The GCA 102 also supported rapid AGP interpretation thathypoglycemia was a clinical priority to be urgently addressed as shownby the indicator of “High” across the entire day.

The third AGP HO (FIG. 4C) illustrates excellent control by achievinglow exposure to both hyper- and hypoglycemia. Given the small margin oferror into the hypoglycemia range, however, there needs to be continualvigilance and awareness of the presence of hypoglycemia. The fourth AGP114 (FIG. 4D) illustrates how the LLG 116 indicated “Red” (widecross-hatched circles), despite having similar hourly 10th percentile118 values to FIG. 4C. This patient would need additional caution due tothe presence of the elevated variability below the median and thepotential to induce further hypoglycemia, especially if the elevatedmedian values were to be addressed by more insulin coverage. In FIG. 4Cthe 10^(th) percentile 120 values of 80 to 90 mg/dL were acceptable,whereas, in FIG. 4D they indicated an added risk because of the highermedian values and the increased risk for hypoglycemia associated withtherapeutic intervention(s) to reduce the median closer to goal. The GCAindicators 122 and 124 encapsulate this clinical logic by linking thedimensions of hypoglycemia, median glucose levels, and glucosevariability below the median.

The grouping of the five median-variability value pairs for thesepatient examples are shown in FIG. 4E. It can be intuited that patient A(FIG. 4A) would have the least likelihood of low glucose, as the medianis high and the variability is low. In this region of the plot, none ofthe 10^(th) percentile values would be lower than 100 mg/dL. Conversely,as median values decrease (patient C) (FIG. 4C), variability increases(patient D) (FIG. 4D), or both occur (patient B) (FIG. 4B), there wouldbe an increased likelihood of low glucose for these patients relative topatient A. This linkage between median, variability, and low glucose isutilized by the method to create the LLG indicator. In the next section,the relationships in FIG. 4E will be discussed in terms of the ControlGrid, a construct that underlies the GCA.

To define the decision support method, there are two important aspectsthat need to be considered for proper glycemic management: (1) reducingoverall glucose levels; and (2) reducing glucose variability in order tominimize inducing hypoglycemia as glucose levels are lowered. The key tothis framework is to consider that for a period of time each patient hasa population of glucose readings that can be described as a stationarystatistical distribution. The Gamma distribution is an appropriate andconvenient model; like glucose values, this distribution does not allowzero or negative values and is skewed toward high glucose values.

For each period of the day, the distribution of glucose values can becharacterized by a pair of metrics representing central tendency andvariability. The median (50^(th) percentile, or P₅₀) was chosen as themetric for central tendency, and the difference between the median and10^(th) percentile (P₁₀) as the metric for variability, also defined asthe lower interdecile range, P₁₀ to P₅₀, and named “Variability belowMedian” on the Insights report 69.

Percentile metrics were favored over other common statistics such asmean and standard deviation because of commonality with AGP's 70 use ofpercentiles, as the GCA 72 is intended to compliment the AGP on theInsights report 69. In addition, percentiles are more robust to outliersthat often occur in glucose data. The P₁₀ to P₅₀ metric was chosen forrepresenting variability instead of other symmetric measures, such asthe interquartile (P₂₅ to P₇₅) or interdecile (P₁₀ to P₉₀) rangesbecause it was a better predictor of hypoglycemia risk.

Using this framework, the mathematical relationship between glucosemedian, glucose variability and LLG can be described. This relationshipled to rules that translated glucose data into GCA indicators to providestandardized guidance for treatment decisions.

For the purposes of this method, a hypoglycemia metric was selected thatis dependent on both time and magnitude of glucose readings below 70mg/dL, referred to as AU70 (area under 70 mg/dL). Taking all readingsbelow 70 mg/dL, the AU70 metric is defined as the sum of all differences(70 mg/dL−reading) divided by their total number. The value of the AU70metric used in generating the Insights report is referred to as the LowGlucose Allowance (“LGA”) setting. As described previously, the reporthas three possible settings defined for LGA (Low, Moderate, or High);each of these configures the algorithm for three different degrees ofrisk for low glucose.

As mentioned, the key concept underlying the decision supportmethodology is the relationship between median, variability, andhypoglycemia risk. This is illustrated on the median-variability plotsshown in FIGS. 5A to 5C, where the glucose median is on the y-axis andthe glucose variability (P₁₀ to P₅₀) on the x-axis. A point was plottedfor each patient for a period of the day (in this example 3 am to 8 am)for two weeks of Navigator® continuous glucose sensor (Abbott DiabetesCare, Alameda, Calif.) continuous glucose measurements. The point is anopen circle (red) if the AU70 metric exceeded the LGA; otherwise, thepoint is a solid circle (blue). The figures show a good separationbetween the two populations of points, even as the LGA is varied.

The Gamma distribution model of the glucose data can be used, along withthe AU70 definition of hypoglycemia, to theoretically derive a boundarybetween these two populations. This boundary, referred to here as the“High Risk Curve” (130 in FIG. 5A, 132 in FIG. 5B, and 134 in FIG. 5C),is made up of points on the median-variability plot that correspond tothe same AU70 value, and can be found using the equation for the Gammadistribution varied over all possible median and variability values tofind those pairs where the area of the distribution below 70 mg/dL isequal to the AU70 setting. Three High Risk Curves, one for each LGAsetting (FIG. 5A has a large “low glucose allowance” (LGA), FIG. 5B hasa medium LGA, and FIG. 5C has a small LGA), have been determined for usewith the Insights report 69. FIGS. 5A to 5C show how well these curvesseparated the two point populations. The LGA settings were determinedfrom a large population of median-variability points, and selected suchthat approximately 10%, 30%, and 50% of the LLG indicators would bebelow the High Risk Curve for the Large, Medium, and Small settings,respectively.

The High Risk Curve 138 of FIG. 6 can be used to divide themedian-variability plot into two zones: a low hypoglycemia risk zone 140above the curve, and a high hypoglycemia risk zone 142 below the curve(FIG. 6). This concept of zones can be extended to convert themedian-variability plot into a so-called Control Grid (“CG”) 145. Usinga patient's set of CGM data, a Control Grid and point on it can becalculated for each of the five time-of-day periods (FIG. 6).

The High Risk Curve divides the “high” (red) and “moderate” (yellow) LLGzones, and a Moderate Risk Curve divides the “moderate” and “low”(green) LLG zones. The Moderate Risk Curve accounts for uncertainty inthe LLG indicator as a result of uncertainty in the values. Theuncertainty of the median and variability (P₁₀ to P₅₀) are affected bythe number of data samples available and the time-varying nature ofthese values. The Moderate Risk Curve 144 was determined empirically foreach time-of-day period and LGA setting, such that patients with pointsin the low risk zone during one two-week period have less than 10%chance, with 95% certainty, of landing in the high risk zone during thesubsequent two-week period. The Moderate Risk Curve is implemented as a60-element look-up table along the dimensions of LGA (3 levels), daytimeor nighttime (2 levels), and amount of glucose measurements (10 levels).For example, the Moderate Risk Curves for 14 days of CGM for a 5-hourperiod of the day have AU70 values of 0.03, 0.17, and 0.53 for daytimeperiods for the small, medium and large LGA settings respectively, and0.02, 0.09, and 0.39 for nighttime periods.

The criteria for the Median Glucose indicator described in FIG. 3 can berepresented visually as a horizontal line at the Median Goal setting.This boundary divides “Green” from “Yellow” indicators, with “Yellow”indicators becoming “Red” if the median value for that time period isboth 20% and 40 mg/dL above the 24-hour median glucose value.

The boundary for the Variability below Median indicator described inFIG. 3 can also be represented as vertical lines in FIG. 6. The HighVariability line 148 is located at the intersection of the Median Goal150 and the High Risk Curve 138. This defines high variability to besuch that the median glucose cannot be reduced below the median goalwithout indicating high LLG. The Moderate Variability line 152 is fixedat 35 mg/dL (1.9 mmol/L); this value was determined by review of CGMstudies in people without diabetes.

The Control Grid 145 identifies different zones according to glycemicconditions of clinical relevance that indicate the direction of therapymodification. The significance for therapy decision support is that thezones provided on the control grid can be the basis for mapping glucosedata for a period of the patient's day into therapy suggestions.Specifically, a point on the Control Grid directly maps to a column onthe GCA 160 (FIG. 7).

The table of FIG. 7 illustrates how the GCA indicators 162 can beapplied to achieve glucose control. See FIG. 10 for the correlationbetween colors, meanings, and symbols used in the drawings. For example,in the first row depicting two “OK” green signals 164, there is a verylow risk of having hypoglycemic episodes, thus no changes are needed asthe patient has met the glucose goals of managing both hypo- andhyperglycemic exposure. A possible action that could be taken is toconsider lowering the glucose goal if the patient is not at the A1Ctarget. The fourth row 166 of FIG. 7 illustrates scenarios with eithermoderate, densely shaded circle (yellow), or high, sparsely shadedcircle (red), risk for LLG. For these patients it would be important toaddress self-care behaviors to reduce glucose variability and the riskfor hypoglycemia. If necessary, insulin doses may need to be adjusted toreduce hypoglycemic risk.

The preceding concepts were applied retrospectively to JDRF-CGM clinicaltrial data to establish hypoglycemia assessment and forecastingperformance. CGM values for all participants were divided into 4-weeksegments starting at the first available sensor reading. Each four-weekperiod was split into 2 two-week periods (1 and 2), and each period wassplit into 5 time-of-day intervals (3 am-8 am, 8 am-12 pm, 12 pm-6 pm, 6pm-10 pm, 10 pm-3 am). The value of AU70 was calculated for all periodsand intervals. These values were taken to be true, and were comparedwith the results from the LLG method and a method based on the P₁₀ valueof a time-of-day interval, using data from period 1 only. Note that theP₁₀ method can be estimated directly from the AGP.

For purposes of analysis, any value of AU70 above 0.83 was deemedexcessive, and any value below 0.83 was acceptable. The comparison ofeither method with the AU70 of period 1 was called “in-sample,” whilethe comparison with the AU70 of period 2 was called “out-of-sample.” TheP₁₀ forecast was “Green” if the 10th percentile was above an upperlimit, “Red” if below a lower limit. A variety of lower and upper limitvalues were evaluated. The combinations of in- and out-of-samplecomparisons were tabulated to compare the performance of the LLG and P₁₀methods.

There were 13,932 evaluable comparisons between low glucose riskforecasts in Period 1 and actual measured low glucose exposure in Period2.

The most important safety performance criterion is minimizing the rateof missed detections of excessive hypoglycemic risk. This corresponds to“Green” indicators when there is excessive AU70 (FIG. 8), as this is afalse indicator that it would be safe to increase insulin to lowerglucose when it would likely induce additional hypoglycemia. The LLGmethod had an in-sample rate of 0.5% and out-of-sample rate of 5.4% forincorrect “Green” indicators across all evaluated periods. These werebetter than the P10 method for upper limits up to 110 mg/dL. Whenevaluated only for the periods with median values 155 mg/dL and higher,the performance of the LLG is even better when compared against the P10method. This performance when the median glucose is elevated abovetarget is an important performance measure, as it is the situation thatwould predominately indicate an increase in insulin coverage for thatperiod, thus carries substantial risk of inducing additionalhypoglycemia.

In terms of correct detection of excessive AU70, the LLG method has anin-sample rate of 88.3% and an out-of-sample rate of 59.6%. These wereas high or higher than the P10 method up to about 80 mg/dL. Whenevaluated for median ranges above and below 154 mg/dL, the P10 lowerlimit that matched the performance of the LLG method was different. Forthe median range above target, the LLG was superior or equivalent up to90 mg/dL, while for the lower median range, the LLG was superior orequivalent up to 75 mg/dL. See FIG. 9. This illustrates a challenge ofusing the P10 method, as limits must be varied according to the medianrange to match the LLG performance. The LLG method did not have thischallenge as it was founded on the inter-relationship of the threeimportant dimensions of median glucose, variability, and hypoglycemia.

The trade-offs associated with the dimensions of superior performance ofthe LLG method included having higher rates of false alarms (incorrect“Red” indicators) and lower rates of true negatives (correct “Green”indicators). The incorrect “Red” indicators for the LLG method was 16.4%in-sample and 23.7% out-of-sample, which was approximately equivalent tothe P10 method with a lower limit of 80 mg/dL for all median levels.However, the LLG method was superior for lower median values (<154),where an incorrect Red may lead to a reduction in insulin coverage. TheLLG method had incorrect “Red” indicators of 20.9% in-sample and 30.7%out-of-sample, which was better than 30.5% in-sample and 38.8%out-of-sample for the P10 method with a lower limit of 80 mg/dL.

Taking all of these dimensions of performance together, the LLG methodstood apart from the P10-based methods in that no single pair of limitscould match the performance of the LLG, in particular when correctlyassessing risks of hypoglycemia when the median glucose level was abovetarget.

Diabetes clinicians traditionally have had to make treatment decisionsbased on infrequent glucose readings that may not adequately reflect apatient's glycemic profile. Continuous glucose monitoring provides anabundance of information about daily glucose patterns. However, the timerequired to review this vast amount of data can strain clinicianefficiency. The Insights report 69 was designed to support diabetesclinicians in quickly understanding the patient's overall glycemiccondition and facilitating safe and effective therapy modifications.From the standpoint of insulin-based treatments, this method providesstandardized guidance for medication adjustment (increase, decrease ormaintain), and highlights the necessity to address self-care behaviorsin order to reduce glucose variability that is elevated to the pointthat it limits managing both hyperglycemia and hypoglycemia.

The key to this framework was to consider that for a period of time eachpatient had a population of glucose readings that could be described asa statistical distribution. A fundamental insight of this model was thedefining of the boundaries between Low, Moderate, and High LLG based onthe gamma distribution. A benefit of this reporting model is that thedecision support algorithm was designed to allow the clinician to adjustthe Low Glucose Allowance setting depending on the aggressiveness oftreatment, allowing for more or less conservative report indicators.

Computerized treatment algorithms using CGM data have been developed inan effort to use the abundant information in a clinically meaningfulmanner. In a two-month study of 22 insulin-dependent subjects usingdaily capture of SMBG and a predictive glucose model, A reduction inrates of hypoglycemia by nine-fold and insulin therapy by −9 U/day wasreported. Computerized glucose programs have also been used foreducational purposes to allow patients to gain insight into the effectof insulin dosage adjustments, diet changes, and exercise on glucoselevels. The algorithm “Librae,” a computerized diabetes simulator indiary format developed as an educational tool for patients, correlatedwell with the CGM data, however there were also some clinicallyunacceptable errors at extremes of blood glucose levels. Theprogrammatic model described herein differs from other models in therobustness of the glycemic forecast comparisons that were used reducemissed detections of excessive hypoglycemia. Two key advances of the LLGmethod are its sensitivity in detecting incorrect green and correct redforecasts for improved predictive capabilities when compared with themethods relying solely on the lines of the AGP. The predictive aspect ofthe proposed algorithm provides clinicians with targeted areas to focuson, such as high risk for hypoglycemia and variability, which in turnsaids in determining how aggressive corresponding treatment should be.The proposed model also displays potential reasons for glycemicvariability that can be addressed with the patient, and used for patienteducation about lifestyle behaviors.

There were some challenges associated with designing the programmaticmodel presented here. For instance, each of the three High Risk Curveswas associated with a single constant value of the hypoglycemia metric,selected to be AU70. Because there is no established guidance on howmuch hypoglycemia area is “excessive” or “problematic”, the AU70settings had to be empirically derived. In a patient exam using theInsights report 69, the clinician would be able to further probe thehypoglycemic experience and assess the need for intervention.Understanding the alignment between these AU70 settings and clinicaldiabetes management needs further investigation, particularly fordifferent patient profiles of diabetes type, age, duration of diabetes,and presence of comorbidities. There are likely instances where more orless hypoglycemia are acceptable based on the needs of the patient. Forexample, an elderly patient who lives alone may need to be more vigilantabout the possibility of a severe hypoglycemic episode compared with ayounger individual who can recognize hypoglycemic symptoms, is trainedto treat hypoglycemia, and is using CGM with low glucose alarms. Thisestablished the clinical need to have multiple settings (large, medium,and small) of LGA based on the characteristics of the patient, butfurther work is needed to understand how to clinically apply andvalidate the available settings.

The vulnerability to low glucose can be higher overnight while sleepingbecause of impaired hypoglycemia symptoms. This motivated the decisionto have empirically derived the Moderate Risk Curve for each timeperiod. This derivation met expectations of resulting in moreconservative settings overnight compared to daytime. Fear ofhypoglycemia has been reported by pediatric and adult populations, aswell as by caregivers, and is associated with increased frequency ofsevere hypoglycemia. Moreover, fear of hypoglycemia may contribute topoor glycemic control, weight gain, and emotional distress. The use ofthe programmatic report described here, which highlights time periods ofincreased risk for hypoglycemia, may be a valuable tool for overcomingfear of hypoglycemia. Further research in the clinical population isneeded to investigate this potential benefit.

The Insights report 69 provides a model for assessment of high risktimes of the day that require therapeutic intervention, and providesmore detail than A1C alone. As shown in FIGS. 2A-2D, patients maypresent with the same A1C value, yet have very different daily glucosepatterns. The Insights report offers pattern recognition capability thathighlights areas of variability. A key feature of this report is thesuggestions for topics to discuss with the patient, which could aid introuble-shooting potential reasons for increased glucose variabilitythat may be associated with elevated hypoglycemia risk.

It has been noted that 27 non-insulin using patients who receivedbehavioral intervention consisting of review of CGM data and “rolemodel” data about exercise benefits showed greater improvements in A1C,moderate activity, systolic blood pressure, and body mass index whencompared with the control group who received generic education andadvice. These results, although in a small population of non-insulinpatients, show the benefits of glucose reports in patient education andtreatment.

The analyses presented here have several limitations. For the LLG versusP₁₀ safety and performance analysis, the time-periods of the day used inthe analysis were at fixed times of the day, not individualized toactual daily activities of patients, and may therefore bias the results.The analysis does not account for the interplay between time periodswhen managing glucose levels. Furthermore, this was a retrospectiveanalysis and was not incorporated into a glucose-control intervention.The performance may vary under the conditions of using the hypoglycemiaforecasts to support clinical treatment decisions. Finally, there was noaccounting for the repeated assessments on the same study participantsover the longitudinal course of the study.

Methods for Identifying Diabetes Self-Care Behaviors of TherapeuticImportance

Frequent glucose monitoring, for example supplied by sensor-basedinterstitial measurement, has expanded the possibility of summarizingand reducing the measurements into metrics of interest for diabetesmanagement. To date, there has been an abundance of data reductionmethods proposed (averages, medians, percentiles, variability metrics,risk metrics, etc.); however these methods have failed to enlighten amajority of care providers and patients. Many patients and careproviders feel overwhelmed and burdened by an excess of data thatprovides no additional insight or knowledge.

The current invention leverages clinically-informed algorithms to searchthe data to reveal insights about the glucose control and self-carebehaviors performed by the patient. These insights can then direct thecare provider and patient to therapeutic and educational methods toimprove diabetes self-care behaviors, improve glycemic control andreduce risks of short- and long-term complications associated withdiabetes.

The invention will be described with sensor-derived glucose measurements(multiple measurements per hour), but has the potential to also beapplied to frequent (four or more per day) strip-based glucosemeasurements.

The current invention uses clinically-informed algorithms to searchglucose data acquired for an individual patient to reveal diabetesself-care behaviors. There are five main components to the operation ofthe invention: 1) defining “episodes” of interest, either dailyactivities or glucose-derived, 2) selecting a “kernel” episode for thesearch routine, 3) constructing “episode chains” of a sequence ofepisodes (including the kernel episode) and logical rules for theinclusion or exclusion of episodes in close proximity to the kernel, 4)associating one or more episode chains with a diabetes self-carebehavior, and 5) displaying the findings of the search algorithms.

Episodes

This invention proposes using episodes related to daily activities(meals, taking medications, exercise) as well as four main classes ofglucose-based episodes: High, Low, Rise, Fall. Each of these glucoseepisodes are defined by thresholds. For each class of glucose episode,several instances (or “flavors”) may be defined for use in the searchalgorithms. For example, two types of “High” glucose episodes may beconstructed: “Extreme High” may have entrance/exit thresholds of 240/220mg/dL and minimum duration of 15 minutes, while a “Moderate High” mayhave entrance/exit thresholds of 180/160 mg/dL and minimum duration oftwo hours. In this way, a clinically-informed hierarchy of severity ofhigh glucose may be formed, such as a clinical statement to a patientof: “Try to never go above 240 mg/dL (“Extreme High”), and try to avoidgoing above 180 for more than two hours (“Moderate High”).” In theexample shown in Table 1, two activity-based episodes (Meal andExercise) and five glucose episodes have been defined: “Low (L)”, “High(H)”, “meal-related Rise (m-R)”, “low glucose treatment Rise (lt-R)” and“Fall (F)”. In the absence of daily activity records, it is envisionedthe search algorithms could be executed on glucose episodes alone.

Kernel Episode Selection

The “episode kernel” is the episode which initiates the search algorithmfor each episode chain, and there is only one kernel per chain.

Episode Chain Construction and Search Logic

Using the kernel as the starting point, other episodes before and/orafter are defined to identify specifically the self-care behaviors ofclinical interest. A duration of time relative to other episodes in thechain would be defined for each “Relative Time Slot”. For example, twohours may be used as the period of time between the beginning or end ofthe episode in each slot and the end or start time of the previous orsubsequent timeslot (respectively). In the example below, all durationsof “Time Slot” was set to two hours, but it is envisioned that the timeslot duration setting may be different for some time slots, or evenunique for each link in each episode chain. Furthermore, the logic mayenforce the absence of one or more episodes in a position of the chain.The presence of an excluded episode would reject the candidate chainfrom being selected as a match to the self-care behavior.

Further logic is envisioned which would need to resolve “overlappingchains”. In these cases, when chains are identified that are coincidentin time, there may be logic which either allows them to both remainidentified for further analysis (allowing the clinician to review andsort out the overlapping), or there may be a hierarchy of importance orprecedence of one chain over another (helping the clinician by removingconflicting self-care behavior activities identified at the same time).

Association of Episode Chains and Self-Care Behavior

One or more episode chains are associated with a clinically-meaningfulself-care behavior. These behaviors would be selected because of therisk they pose to the patient and/or the possible interventions(medications, education, etc.) which may be offered to reduce the futureoccurrence of the episode chain(s).

Display of Search Output

The number of episode chains and self-care behaviors found by the searchalgorithms could be displayed as a “scorecard”, indicating whichself-care behaviors were most prevalent. In addition, comparison tohistorical findings for that patient may be shown. Alternatively, acomparison of self-care behaviors needing improvement for a particularpatient could be compared to a population of similar patients. Thesedisplays would enable efficient sorting of potentially effectiveinterventions to reduce the number of self-care behaviors problemsexperienced by the patient. It is envisioned that the display of theresults would also provide access to further details and guidance forexpert- or evidence-based techniques for addressing these self-carebehaviors in a positive way.

In order to provide further insight into the timing and potentialpatterns of the self-care behaviors experienced by the patient, theepisode chain or chains associated with each behavior may be shown in atime-of-day plot, with each episode indicated within the chain. Forexample, a 24-hour plot may be used, or a 48-hour plot may be used toensure that episodes that occur after midnight on the day of the kernelepisode are shown to be after the kernel, as opposed to “wrapping” tothe morning period. The start time of the kernel episode would beindicated to provide reference to the other episodes in the chain. As analternative display method, the chains could be displayed along a timeaxis that is referenced to the start time of each kernel episode of eachchain type or self-care behavior. This format has the potential to beinstructive to the clinician and patient about the recurringcause-and-effect relationships of the episodes of interest.

TABLE 1 Episode and Logic Nomenclature Definitions Episode TypeDefinitions: Meal A patient-recorded meal event Exer A patient-recordedexercise event L Low glucose episode H High glucose episode m-Rmeal-related glucose Rise (starts above 75 mg/dL) it-R low glucosetreatment Rise (starts below 75 mg/dL) F glucose Fall to below 100 mg/dlLogic Definitions: not Prefix meaning that episode type is not in therelative-time window bolded This episode is the “kernel” of the searchroutine, which starts the search algorithm | “or” for more than oneepisode type in the chain position & “and” for more than one episodetype in the chain position Time Slot amount of time before/after anepisode to look for other episodes of interest in the chain

TABLE 2 Logic definition for identifying self-care behaviors EpisodeChain Relative Time slot Inappropriate Diabetes Self-care Behavior 1 2 34 5 Meal-to-Insulin Amount notF m-R H — — Mismatch, too little insulinMeal m-R | lt-R NotF — — Meal H — — — H Meal H — — H m-R NotH — —Meal-to-Insulin Timing notF m-R NotH — — Mismatch, insulin too late Mealm-R !lt-R F — — H p not m-R & not lt-R — — High overtreatment. H L notH— — without rebound High H F notH NotH& H Meal F Not m-R — H lt-R notH —— High overtreatment, with H L H — — rebound High H lt-R H — — H F m-Rl/t-R — — H Meal F H — H Meal F m-R — H lt-R — — HiQh undertreatment Hnot L & not lt-R H — — Exercise-induced glucose Exer F — — — drop Exer L— — — Isolated High, too little not L & not Jt-R — — Insulin & not H &not not L & not lt-R & — — Meal H not H & not Meal — — Rescue Carb LMeal — — Low overtreatment, notH lt-R siotL — — without rebound Low Fm-R siotL — — L lt-R notL — — notH L H notL — Low overtreatment, withnotH lt-R L — — rebound Low F m-R L — — L lt-R L — — L H L — — Lowundertreatment L not H & not lt-R L — — Isolated Low, too much not L &not lt-R not L & not lt-R & — — insulin &notH L not H & not Meal — —

Detailed example of application of invention:

Subject DNB0509, 8Dec.06-14Dec.06

Table 3. Time Slot: 2 hours (universal applied to all time-slots betweenepisode chain links) Kernel Start Time Description (bolded indicateskernel episode) 9 Dec. 2006, 5:49 AM: m-R, H: Meal-to-Insulin AmountMismatch, too little insulin 9 Dec. 2006, 4:59 PM: m-R, not H:Meal-to-Insulin Timing Mismatch, insulin too late 9 Dec. 2006, 10:20 PM:H: Isolated High, too little insulin 10 Dec. 2006, 1:59 PM: L, Meal:Rescue Carb 10 Dec. 2006, 2:11 PM: not H, lt-R, not L: Low overtreatmentwithout rebound Low 10 Dec. 2006, 10:20 PM: Meal, H: Meal-to-InsulinAmount Mismatch, too little insulin 12 Dec. 2006, 8:23 AM: H, Meal, F,not H & not m-R: High overtreatment, without rebound High 13 Dec. 2006,3:55 AM: not H, lt-R, not L: Low overtreatment without rebound Low 13Dec. 2006, 6:56 AM: m-R, H: Meal-to-Insulin Amount Mismatch, too littleinsulin 13 Dec. 2006, 9:32 AM: H, Meal, F, H: High overtreatment, withrebound High 13 Dec. 2006, 1:26 PM: not H, lt-R, not L: Lowovertreatment without rebound Low 13 Dec. 2006, 2:56 PM: m-R, H:Meal-to-Insulin Amount Mismatch, too little insulin 13 Dec. 2006, 6:36PM: F, m-R, not L: Low overtreatment without rebound Low 13 Dec. 2006,8:06 PM: m-R, not H: Meal-to-Insulin Timing Mismatch, insulin too late14 Dec. 2006, 9:37 AM: H, Meal, F, m-R: High overtreatment, with reboundHigh 14 Dec. 2006, 2:17 PM: F, m-R, not L: Low overtreatment withoutrebound Low 14 Dec. 2006, 2:19 PM: Meal, m-R, F: Meal-to-Insulin TimingMismatch, insulin too late

TABLE 4 Summary Table, “Self-Care Behavior scorecard” IdentifiedSelf-Care Behavior Count Low overtreatment without rebound Low 5Meal-to-Insulin Amount Mismatch, too little insulin 4 Meal-to-InsulinTiming Mismatch insulin too late 3 High overtreatment with rebound High2 High overtreatment without rebound High 1 Isolated High, too littleinsulin 1 Rescue Carb 1 Grand Total 17

Illustrative views are presented in FIGS. 11 through 18 in which areshown overlays of behavior patterns on graphs where the vertical showsglucose in mg/dL and the horizontal shows time in a thirty-hour span.Both FIGS. 11 and 12 are related to self-care patterns while FIGS. 13-18show CGM data overlayed with patterns. For example, in FIG. 13, toolittle insulin was given at 5:49 am, insulin was given too late at 4:59pm, and too little insulin was given at 10:20 pm.

Glucose Metric Mappings to Diabetes Treatment Recommendations

The invention provides a means to convert glucose data into clinicallyrelevant treatment decisions and the means to map metrics generated fromglucose results to treatment recommendations that take into accountminimizing the risk of hypoglycemia is described.

The goal is to determine the appropriate therapy modification for apatient based on the measured glucose data. The metrics used are glucosemedian and glucose variability, calculated for a specified period oftime. Variability (or volatility) may be estimated using many differentpossible metrics—for this description, the lower 40% percentile is usedto represent variability. Median is chosen as it is less sensitive tooutliers than the mean. However, any metric that would represent centraltendency of data may be readily used here.

The Glucose Control Grid

The glucose median and variability may be illustrated graphically where,for instance, the median is represented along the y-axis and thevariability is represented along the x-axis. As will be described, thisgraph will be divided up into zones that represent possible treatmentrecommendations. This graph is called the Control Grid. These zones canbe represented mathematically and implemented in software to provideautomated therapy recommendations based on glucose data, as will bedescribed. In addition, the Control Grid itself may be displayed to the“HCP” (healthcare provider) and/or patient by the software.

One version of the control grid is illustrated in FIG. 19. A patient'sglucose median and variability can be plotted on this grid. Also, theuncertainty in the estimate of the median and variability can be plottedhere, for instance, as represented by a cloud of points, or a “bubble”,or some other representation, as described previously. These metrics arecompared to the lines defining the zones, as will be described below.Note that there are many possible metrics that can be used forcomparison, such as the centroid or “best estimated” of the metric, orthe 95% confidence point of the metric (referred to as the TreatmentRecommendation Point—“TRP”), as illustrated in FIG. 20. Other possiblemetrics can be readily contemplated.

On this particular control grid shown in FIG. 19 there are four zonesdefined. The Hypo Risk zone is defined as the region below the hypo riskline where it is determined that, if the TRP falls below, the patient isat an unacceptable risk of hypoglycemia. In this case, the displayedtreatment recommendations would be related to reducing the patient'sglucose variability and/or increasing the patient's glucose median. Forinstance, one specific recommendation related to reducing glucosevariability would be for the patient to eat more regularly. A specificrecommendation related to increasing glucose median would be to reducethe dose or dose rate of glucose-lowering medication.

The Target zone is the ultimate goal for the patient and HCP. The Targetzone is defined as being above the Hypo Risk line and below a Targetline—the Target line can be adjusted by the HCP to provide an achievabletreatment goal appropriate for a particular patient. The preferredembodiment of the logic is that the patient is in the Target zone if a)the TRP is not below the Hypo Risk line and b) the metric centroid fallswithin the Target zone.

The Buffer zone is defined as the region above the Target zone and theHypo Risk zone, but below a line defined as an offset above the HypoRisk zone. This offset is representative of the possible or expecteddrop in median due to an increase in glucose-lowering mediation. Thiszone represents the region where, if the TRP was contained within it, itwould be unsafe to recommend an increase in medication, since it maydrive the patient into the Hypo Risk zone, assuming that glucosevariability did not change. In this case, the displayed recommendationwould be related to reducing the patient's glucose variability.

The “Safe to Titrate” zone is defined as the region where the TRP isabove the Buffer zone and above the Target zone. Here the recommendationwould be related to increasing the patient's glucose-lowering medicationdose in order to reduce their median glucose. The logic diagram in FIG.21 illustrates the mapping described above.

The Control Grid can be fashioned a number of different ways. Forinstance, what has been described as straight lines may be moreappropriate to describe as curves, for instance, for the Hypo Risk line.As another example, a Control Grid design is shown in FIG. 22 whichillustrates two modifications to the previous Control Grid example. Thefirst modification is to remove the Buffer zone and replace it withrecommendations displayed that specifically indicate the distance fromthe Hypo Risk line. For instance, for a TRP that is located 30 mg/dLabove the Hypo Risk line in the “Safe-to-Titrate” zone, therecommendation may read “Increase dose of glucose-lowering medication,margin to safely reduce median glucose is 30 mg/dL”. The modifiedrecommendations can indicate both the margin above and below the HypoRisk line. For instance, for a TRP that is located 10 mg/dL below theHypo Risk line, the recommendation may read “Decrease dose ofglucose-lowering medication, a 10 mg/dL increase in median glucose isneeded to reduce hypoglycemia risk to a safe level”. Alternatively, therecommendation may indicate the positive or negative horizontal distancefrom the Hypo Risk line, in terms of variability reduction. Combinationof these can also be contemplated. The main reason to eliminate thefixed buffer zone is that dose increments may achieve different glucosemedian reductions, depending on the medication used or the patient'sphysiology. Another embodiment is to provide a mechanism where the HCPcan modify the Buffer zone depending on these factors that could impactglucose median reductions.

The second modification to the Control Grid shown in FIG. 22 is theaddition of a vertical Variability line used to drive variabilityrelated recommendations. Here, some or all of the zones are furtherdivided into sub-zones. In the sub-zones where the centroid metric is tothe right of the Variability line, variability related recommendationsare provided. Where the centroid metric is to the left of theVariability line, variability related recommendations are not provided.The Variability line may be defined as fixed at a specific location onthe x-axis; that is, at a specific variability value. The preferredembodiment is to for the x-axis location of the Variability line todepend on the Target line and/or the Hypo Risk line. For instance, thelocation may be determined by the intersection of the Target line andthe line defined as 50 mg/dL above the Hypo Risk line. This provides forvariability recommendations appropriate for the target set for aspecific patient.

Another example of a control grid includes inclusion of a buffer zone atan offset above and/or below the Hypo Risk line. For instance, if theTRP is within this zone, then the recommendations would not include arecommendation for medication adjustment. Outside this zone, therecommendation would include a medication adjustment recommendation.Another example is a zone defined by the Hypo Risk zone divided by theTarget line. For a centroid metric above this line, the recommendationwould not include decreasing medication, but below the line, therecommendation would include decreasing medication. With these examples,it is clear how alternative zones can be designed and utilized.

Zones may also indicate multiple recommendations at varying degrees ofimportance. The degree of importance may be indicated by the order inwhich they are listed, or by color coding the recommendations, or by anyother appropriate means.

Recommendations may also include other factors not directly related totreatment. For example, the recommendations may pertain to the need toincrease SMBG (self-monitored blood glucose) sampling frequency.Additional sub-zones can be included in the control grid, for instance,such that when the TRP is below the Hypo Risk line, but the centroidmetric is above the Hypo Risk line, the recommendation includesreduction in variability and the need to increase sampling frequency inorder to reduce uncertainty in the metric. The sampling frequencyincrease recommendation can also be generated by comparing the size ofthe “uncertainty bubble” to a predetermined size and if the bubblecrosses one or more of the lines on the grid, then an increase insampling frequency is recommended. Various measures of “uncertaintybubble” size can be contemplated, including a figure of merit of thedistance between the centroid and the TRP.

Configuration of Control Grid Logic

In a further aspect, it is contemplated that the parameters of theControl Grid may be modified by the HCP. The software that implementsthe automated therapy recommendation logic would provide a means, suchas a popup screen, for the HCP to alter the lines on the Control Grid,or select certain features of the Control Grid. A preferred embodimentis to allow the HCP to select from a pick list of possible Target levelsand Hypo Risk levels. The Target levels on the list may be associatedwith various diabetes complication statistics such as corresponding A1c.For instance, it may be more acceptable for a patient with A1c of 10% tohave a near-term target of 9% rather than 7% so as not to bediscourages. The Hypo Risk levels may be adjusted as necessary to tailorto a patient's tolerance of hypoglycemia. The Hypo Risk pick listlabeling may be associated with expected frequency of hypoglycemia, arelative measure of hypoglycemia risk such as High, Medium, Low, or anyother appropriate labeling. In the software, the Recommendationalgorithm may be initially run with default parameters (eitherpredefined in the code or set to the last algorithm run for that patientfrom a previous doctor's visit). A popup window would be provided toallow the HCP to alter one or more of these algorithm input parametersas needed, and the algorithm is rerun, generating new recommendations.

Control Grid by Time of Day

Another aspect of this invention is to use the Control Grid basedalgorithm to process data for specific time periods of the day orrelative time periods related to key events. For example, four key timeperiods can be defined as overnight/fasting (12 am-8 am), post breakfast(8 am-12 pm), post lunch (12 pm-6 pm), and post dinner (6 pm-12 am).Glucose data collected for multiple days can be grouped into these timeperiods and the Control Grid algorithm run for each group. This isuseful for generating recommendations that are specific to time periods.For instance, variability recommendations may be generated specific tomeals or overnight. For patient's whose treatment is multiple dailyinjections (MDI) of insulin, the time-period targeted recommendationsmay be specific to insulin needs during these times of day. Forinstance, the Control Grid for the over-night/fasting period mayindicate that medication dosage should be increase; the recommendationmay indicate that the patient's long-acting insulin dose should beincreased.

The treatment recommendation logic may be more complicated when multipleControl Grids are used. An example of this logic is shown in the FIG.23.

Alternatively to fixed time periods, the Control Grid algorithm can beapplied to time periods defined relatively to events. Specifically, datagrouping can be determined, for example, a) 4 hours past breakfast, b) 4hours past lunch, c) 4 hours past dinner, and d) 4-10 hours past dinner.Various permutations of this example can be imagined. The data groupswill then be processed by the multiple Control Grid algorithm asdescribed above.

Second-Stage Logic to Drive Recommendations

An augmentation of the treatment recommendation described above usingthe Control Grid algorithm is to provide second-stage logic to furthernarrow the possible recommendations that can be made. For instance,there are many different recommendations for reducing glucosevariability, such as “stop snacking”, “don't forget to take yourmedication”, “don't miss meals”, “adjust correction dose of insulin”. Aglucose control zone may be associated with a number of theserecommendations. A second stage of logic may be used to narrow down thelist of recommendations. Detection of episodic patterns, as describedelsewhere, can be used in this second stage to narrow the list ofrecommendations. For instance, if an instance of low fasting glucose isdetected preceded by a post-dinner high glucose, this may be anindication of occasional correction dosing to mitigate a high glucosevalue, and the logic could direct the recommendation to only include“adjust correction dose of insulin”. The logic may require a certainfrequency of occurrence of an episodic pattern.

Recommendation Structure and Logic Integrated with Treatment Stage

The mapping of glucose data to treatment recommendations may beimplemented with the use of a lookup table. The inputs to this table arethe output of the Control Grid analysis and the current treatment andtreatment stage. The outputs are recommendations of different types thatare displayed. FIG. 24 shows a simple example of a treatmentrecommendation lookup table. Notice that multiple recommendations can beassociated with a single input combination. The concept of a lookuptable can be easily extended to more complex glucose metric torecommendation mappings.

Recommendations can take the form of text that is directly displayed, asindicated in the column labeled “Recommended Text” in FIG. 24. They canalso take the form of links to source documents and specific pages ofthe source documents. The content of these source documents may providemore detailed instructions regarding treatment changes. For instance,for a recommendation to change dosage of a medication or a change intreatment, the source document may be published instructions formedication start and adjustment, and the link could be specified topresent the appropriate page of these instructions. Another form ofrecommendations could be questions displayed to guide the HCP ininterviewing their patient to uncover underlying issues in self-caremanagement. These questions could be in the form of text to be directlydisplayed, or reference material. Recommendations may also take the formof guidance about testing frequency or how to alter algorithm inputparameters. The key benefit here to the user is that this information istargeted based on analysis of the patient's glucose tests.

Note that, as illustrated in FIG. 24, recommendations can be tailored tocurrent treatment.

Additional types of recommendations or outputs associated with theinputs to this table can be implemented, including for instance, linksto sources of definitions, links to appropriate pages of a user guide,or links to graphical displays of the data appropriate to illustrate theglucose analysis finding and recommendation. The links could beinstantiated by the user via buttons (the software would need to place abutton associated with the recommendation when needed), or they could beinstantiated similarly with a hotspot, or could automatically presentthe linked information in a popup window or a window dedicated for thisinformation.

The structure for the lookup table may be altered when recommendationsare to be provided based on multiple time-of-day periods. This could bedone using multiple tables or incorporating multiple algorithm resultinputs and multiple associated groups of recommendations into a singletable.

As noted previously, if a second stage of logic is employed, the lookuptable needs to be adjusted to accommodate this. For example, ifhypoglycemic risk is detected in 3 of the 4 time-of-day periods, ratherthan display a separate recommendation related to reducing hypoglycemicrisk for each time period, the second stage logic would map these into ageneral recommendation and indicate that it applies to the three timeperiods.

Glucose Episode Detection System and Method

A robust search system and method are described for identifyingclinically relevant glucose episodes from strip- and sensor-derivedglucose measurements. This is an improvement of existing data analysisand report generation systems and methods present in informaticssystems. This invention proposes methods to search glucose data forepisodes of interest. Existing informatics software typically focus onoverall summary statistics, such as median and percent of readings intarget. Collecting clinically-meaningful glucose episodes and doinganalysis on those provides a higher-level view of the data and mayprovide more actionable information.

The present invention addresses the difficulties encountered (e.g.briefness/outliers, gaps, noise) in searching frequent (say every 1 to30 minutes) glucose values to detect extreme episodes of clinicalinterest. Therefore, the episode search algorithm results can be moreclinically meaningful. In addition, this invention specifies theproperties of episodes that can be clinically meaningful. Theseproperties can also be used to construct sequences, or “chains”, ofepisodes that have specific clinical meaning related to self-carebehaviors. See FIG. 25 for a block diagram of an embodiment of athreshold-based episode detection algorithm in accordance with aspectsof the invention.

The core logic of episode analysis falls into two families:threshold-based, and change-based. Looking for episodes in bothdirections suggest four basic episode types:

1. Low Glucose/Hypoglycemia (readings below a threshold) (see FIG. 26)

2. High Glucose/Hyperglycemia (readings above a threshold) (see FIG. 27)

3. Glucose Fall (rate of change more negative than a (negative)threshold) (see FIG. 29)

4. Glucose Rise (rate of change more positive than a (positive)threshold) (see FIG. 30)

In addition, when looking for sequences, or “chains”, of episodes, it isforeseen to be useful to also define a “within target” episode, whereglucose values are maintained between an upper and lower bound for aperiod of time. Detection of these episodes can be done by extension ofthe threshold-based episode detection algorithms. See FIG. 28 for ablock diagram of a change-based episode detection algorithm inaccordance with aspects of the invention.

Threshold-Based Episodes

The simplest form of threshold-based logic would be to just group allconsecutive points (above/below) a threshold into an episode. Thisinvention improves on this approach to address the following challenges:

Very brief episodes/outlier values are not clinically relevant—Thepresent invention manages this challenge by requiring a minimum numberof readings and/or a minimum duration and/or a minimum area outside thethreshold to consider the episode for analysis; an episode failing anyof the requirements is ignored.

Gaps (periods of time lacking readings) in the data can significantlyalter episode durations—The present invention manages this by setting amaximum gap duration. Any gaps longer than the maximum result in theepisode spanning the gap are split into two separate episodes that areeach analyzed, assuming that they individually meet all analysiscriteria.

Noise in the signal will cause many episodes to be recorded when thetrue value is close to the threshold—The present invention manages thisby defining an exit threshold inside (less extreme than) the episodethreshold. This serves to debounce the signal, because the episode isonly terminated following a threshold crossing if the signal alsocrosses the exit threshold.

Properties of threshold episodes, as so defined, can be defined forclinical utility, including but not limited to: threshold value, mostextreme value (magnitude of excursion past threshold), episode duration,or episode area. This provides a virtually limitless catalog of episodetypes, each of which, if independently clinically relevant, could formthe basis for reports and analysis.

Visual Representation of Relative Positions of Thresholds: In Episode(Hyperglycemia)

-   -   Hyperglycemic Episode Threshold

Between Thresholds (Hyperglycemia)

-   -   Hyperglycemic Exit Threshold

Not In Episode

-   -   Hypoglycemic Exit Threshold

Between Thresholds (Hypoglycemia)

-   -   Hypoglycemic Episode Threshold

In Episode (Hypoglycemia) Example Pseudocode Implementation ofThreshold-based Episode Detection Algorithm

  //”State” is the previous condition, “PointState” is the condition for the new point void BuildList( ) { EpisodeState State = NotInEpisode;  For Each CGMValue In Database  EpisodeState PointState = GetEpisodeState(CGMValue)  if (PointState == InEpisode)   {   if (Gap from previous point >= maximum gap)    {    //end of possible episode     //if it passes all checks...     if (ReadingsInEpisode >= MinimumReadings      && EpisodeDuration >= MinimumDuration      && EpisodeArea >= MinimumArea)      {      //add it to the list of episodes      }    //Start of possible episode    //record start time, reset point count and cumulative area    }   if (State == NotInEpisode)     //Start of possible episode    //record start time, reset point count and cumulative area    }   else if (State == InEpisode)    {    //continuation of possible episode    //push back end time, increment point count, add to cumulative area   }    else // if (State == BetweenThresholds)    {    //debounce region    }    State = InEpisode;   }  else if (Point State == NotInEpisode)   {   if (State == BetweenThresholds ∥ State == InEpisode)    {    //end of possible episode     //if it passes all checks...     if (ReadingsInEpisode >= MinimumReadings      && EpisodeDuration >= MinimumDuration      && EpisodeArea >= MinimumArea)      {      //add it to the list of episodes      }    }   State == NotInEpisode;   }  Next CGMValue }

Change-Based Episodes

The simplest form of change based logic would be to group allconsecutive monotonically increasing/decreasing points into an episode.This invention improves on this approach to address the followingchallenges:

Changes small in magnitude are not meaningful—the present inventionmanages this by requiring the core of the episode to have a rate ofchange that exceeds a threshold. The core of the episode is the set ofpoints that initially trigger the analysis, when two points are foundthat have a high enough rate of change for a long enough time betweenthem, they form the core of an episode which is expanded by scanningoutwards for local extrema.

Signal variation exaggerates the rate of change of very briefepisodes—the present invention manages this by enforcing a minimumduration over which the rate of change must exceed the threshold.

Gaps (periods of time lacking readings) in the data can significantlyalter episode durations—the present invention manages this by setting amaximum gap duration. Any gaps longer than the maximum result in theepisode spanning the gap are split into two separate episodes that areeach analyzed, assuming that they individually meet all analysiscriteria. All of the points before the gap are considered a complete(potential) episode with the last point being the point preceding thegap. All the points after the gap form the start of a (potential) newepisode.

Noise in the signal breaks the monotonicity of the change during periodsof relatively slow change—the present invention manages this by mergingepisodes that are close together into a single episode. The result ofthe merge is a newly defined episode containing all of the pointsbetween the first point of the first episode and the last point of thesecond episode, inclusive.

Episodes merged in this way could have intermediate extreme pointsoutside of the end values—the present invention manages this is byredefining the start and end of the episode to be the most extremepoints anywhere in the newly merged episode.

Episodes redefined in this way could include spikes caused by twoclosely spaced points where one of which is an outlier—the presentinvention manages this by enforcing the minimum duration criteria(rejecting those that do not meet the criteria).

Properties of change episodes, as so defined, can be defined, includingbut not limited to: maximum rate, delta (highest-lowest values), lowestvalue, and highest value. This provides a virtually limitless catalog ofepisode types, each of which, if independently clinically relevant,could form the basis for reports and analysis.

Example Pseudocode Implementation of Change-Based Episode DetectionAlgorithm

  void BuildList( ) {  For Each FirstValue In Database  For Each NextValue In Database (Starting at FirstValue)      if (Distance between NextValue and point before it > MaxGap)       {     if (Last Episode passes checks)     {     //log last episode     }         FirstValue = NextValue        Next FirstValue        }   else if (GetRateOfChange(FirstValue, NextValue) > Threshold)    {    Starting Value = ScanBackForLocalExtrema(FirstValue);    Ending Value = ScanForwardForLocalExtrema(NextValue);    HighestValue = FindMaxBetween(StartingValue, Ending Value);    LowestValue = FindMinBetween(StartingValue, Ending Value);    Starting Value = (HighestValue or LowestValue);    Ending Value = (LowestValue or HighestValue);    if (StartingValue is close enough to EndingValue of last episode)    {      //merge with last episode     }     else     {     if (Last Episode passes checks)      {       //log last episode     }      //store this episode as last episode for next pass     }   }   Next NextValue  Next FirstValue }

Using Glucose Medial and Variability Metrics to Detect ProlongedHyperglycemia Risk and Provide Guidance to Modify Treatment

The use of the control grid concept (glucose median vs. glucosevariability) to associate glucose readings with risk of prolongedhyperglycemia and to direct treatment guidance.

A patient's state of glucose control can be assessed in terms of twosimple metrics. The first relates to the ability to maintain a desirableglucose level on average. The second relates to the ability to minimizethe glucose excursion in the presence of meals and other factors. Amethod to graphically present these two metrics was previouslydeveloped. In one embodiment of this aforementioned graphicalrepresentation, median glucose is the first metric, and the differencebetween the median and the 10th percentile glucose is the second. Thisgraphical representation, called glucose control chart, is shown in FIG.31. The first metric is shown on the y-axis, and the metric concept isshown on the x-axis.

In addition to the patient's state of glucose control, other clinicallyrelevant information can be provided to enhance one's understanding ofthe impact of a planned treatment on the patient's various clinicalstate. Two clinical risks exist, namely risk of retinopathy due to longterm high average glucose, and risk of acute hypoglycemia.

This invention provides extensions in which risk of hyperglycemia andaccumulated high average glucose, are further elaborated. Risk ofhyperglycemia and its link to the glucose control chart is derived in asimilar manner as that of the risk of hypoglycemia. Risk of accumulatedhigh average glucose can be separated into long (i.e. in the course ofmonths or more) and medium (i.e. in the course of half a day or more)term exposure to high average glucose.

An example of the risk of long-term accumulated high average glucose,the risk of retinopathy. Other long-term risks such as the risk ofnepropathy, neuropathy, macrovascular disease, and microalbuminuria, aretied to the patient's HbA1c, and thus can be linked to the glucosecontrol chart in the same manner for the risk of retinopathy. An exampleof the risk of medium-term accumulated high average glucose, such as DKA(diabetic ketoacidosis), and the linking of such risk to the glucosecontrol chart, is described in this disclosure.

Long-term complications cause major morbidity and mortality in patientswith insulin-dependent diabetes mellitus. Studies have established theseclinical risks with measurable markers, where an association betweenlong-term complications and HbA1c are often made. For example,associations between HbA1c and risk of progression of retinopathy, andbetween HbA1c and risk of severe hypoglycemia, are shown in FIGS. 32Aand 32B.

A patient's state of glucose control, represented by a single point inthe glucose control chart for each patient, can be assessed relative tolong term complications (that are associated with long-term exposure tohigh glucose) and hypoglycemia risk. The remaining two types of risk,risks associated with medium-term exposure to high glucose such as DKA,and hyperglycemia risk, requires a slightly different approach. Anexample of multiple risks overlaid on a glucose control chart isprovided in FIG. 33.

Method to Link Medium-Term Exposure to High Glucose (e.g. DKA) toGlucose Control Chart

The development of DKA risk lines require the knowledge of the number ofDKA events over a fixed time period for each subject data. Then, the DKAevent count over the fixed period of time, or equivalently the DKAfrequency for each patient, are paired to the median and variabilityglucose values for each patient. A surface fit of the DKA risk (in termsof DKA frequency) is made based on these patient data.

The difference lies in obtaining the DKA frequency. Since DKA is anindirect result of glucose, where DKA occurs when a patient's-hydroxybutyrate (—OHB) level exceeds 15 mmol/L[ ], an estimate of —OHBlevel based on each patient's glucose time series is calculated. FIGS.4A and 4B illustrate examples of the time relationships between bloodglucose and Plasma 3-OH+butyrate (—OHB) upon suspension of insulininfusion on T1DM subjects. Using study data such as this, a linear, timeinvariant (LTI) transfer function model that maps glucose to —OHB can beconstructed. Then, this model can be used to traverse through eachpatient's glucose data time series, in order to produce estimates of—OHB over the period where each patient's glucose data is available.Similar to counting the number of hypoglycemic events on the glucosetime series, one can count the number of times the —OHB level exceeds 15mmol/L. For each patient data, this results in their DKA frequency canthen be further implemented to obtain DKA risk lines.

Method to Link Hyperglycemia Risk to Glucose Control Chart

Again, the development of hyperglycemia risk lines require the knowledgeof the number of hyperglycemic events over a fixed time period for eachsubject data. Then, the hyper event count over the fixed period time, orequivalently the hyper frequency for each patient, are paired to themedian and variability glucose values for each patient. Then, similar tothe hypoglycemia fit, a surface fit of the hyperglycemia risk is madebased on these patient data.

With lines associated with hyperglycemia risk included in the controlchart, a zone of hyperglycemia risk is defined and treatmentmodifications may be associated with this zone. Specifically, if themedian vs. variability point falls into this zone, treatmentmodification may be recommended to help the patient avoid this zone,similar to what has already been disclosed with regard to hypoglycemiarisk zones. And like what has already be described for hypoglycemia riskzones, the Treatment Recommendation Point may be used to determine ifthe zone is indicated, as opposed to the best estimate of the median andvariability.

Insulin Titration Using Glucose Median and Variability Metrics to AvoidHypoglycemia

Glucose median and variability are used in a “smart” insulin titrationalgorithm that gets patients in target faster than standard titrationtechniques and is less likely to cause hypoglycemia.

Insulin titration algorithms provide a means for the diabetes patient toincrementally adjust their insulin doses until their glucose levels arewithin target range. Titration algorithms typically rely on a very smallamount of SMBG test data (for some algorithms, as few as one reading) tomake titration decisions, which means that often the titration directionrecommended is in error. In order to minimize the likelihood ofhypoglycemia occurrences that might occur due to these recommendationerrors, traditional algorithms use titration increments that are a smallfraction of their total daily dose. Then if one or two titrationdirection mistakes occur, the net change in dose is small and unlikelyto cause hypoglycemia. The result is that it can take a long time,typically twelve weeks or more, to achieve target glucose levels andoptimal insulin dosing. Also, for titration algorithms that rely onepisodic SMBG testing, hypoglycemia occurrences will still occur sincelong periods of time are not accounted for by the sparse sampling.Finally, traditional titration algorithms do not explicitly identifyglucose variability problems that may be preventing successful titrationto achieve glucose targets—high variability may prevent reductions inmedian glucose levels without causing undesirable hypoglycemia risk.

The titration algorithm invention described here uses statisticalmethods to provide titration guidance such that glucose targets arereached in less time, with less likelihood of hypoglycemia. Theinvention also provides a means to indicate to the patient and doctorwhen glucose variability may be preventing successful achievement ofglucose targets.

The “control grid” is a technical method used to generate treatmentrecommendations from glucose readings. The control grid is a plot ofmedian glucose Gm vs. glucose variability Gv (for example, the distancebetween the median to the lower 10th percentile), with sections definedthat are attributed to glucose recommendations. One important aspect ofthe control grid is referred to as the “Hypo Risk Line” (FIG. 35). Thisline is associated with an expected number of hypoglycemia occurrencesfor a given glucose median and variability. In addition, along with thebest estimate of the median and variability, the 95% certainty “bubble”can be plotted on the control grid—if this bubble is above the hypo riskline, then the patient should anticipate, with 95% certainty, not toexceed the frequency of hypoglycemia occurrences associated with thehypo risk line (otherwise referred to as the “Hypo Risk Tolerance”). Thepoint on the bubble closest to the hypo risk line is the “TreatmentRecommendation Point” or TRP. If the TRP is above the hypo risk line,then the recommendations are consistent with safely increasing insulinor other medications that are known to cause hypoglycemia. If the TRP isbelow the hypo risk line, then the recommendations are consistent witheither decreasing insulin or not adjusting insulin, and/or taking stepsto reduce glucose variability. (Note that the “certainty bubble” conceptis only used as an intuitive graphical illustration as does not exactlydescribe how the 95% certainty calculation is performed.)

The vertical difference between the TRP and the hypo risk line isreferred to as the Margin To Treat (MTT). For a given glucosevariability, a positive value for the MTT (that is, the TRP is above thehypo risk line such as illustrated by the vertical distance marked bythe δ in FIG. 36) corresponds to the amount of reduction in the medianglucose that can occur before the TRP crosses below the hypo risk line.This can be associated with the amount of insulin increase allowedbefore causing a high risk of hypoglycemia, as defined by the hypo riskline. A negative value for the MTT (such as illustrated by the verticaldistance marked by the red δ in FIG. 36) corresponds to the amount ofincrease in the median glucose needed for the TRP to be above the hyporisk line and is associated with the amount of insulin decrease neededto transition to a low risk of hypoglycemia.

This invention utilizes the MTT as the metric to drive insulintitration, in such a way to manage the risk of hypoglycemia. The MTTwould be calculated at the end of each titration period based on theglucose readings measured during this period, and the MTT would be usedto determine the insulin change recommendation. Another titration periodwould commence where more glucose readings would be received, and at theend of the period, again the MTT would be calculated and used todetermine the insulin change recommendation, and so on.

The advantage of using the MTT is that it not only provides thedirection of the titration (increase or decrease) but also amount of thetitration, in the form of desired glucose median change. Since theglucose for different diabetes patients responds differently for a givenchange in insulin dosage, the MTT cannot be used directly to drivetitration amount. Another aspect of this invention is that the titrationalgorithm will learn how a change in insulin affects the median glucosefor a specific patient, and will use this measured affect to convert theMTT to a specific insulin change amount. In the first embodiment, thefirst titration amount may be preset to correspond to a conservativevalue defined by predetermined patient information such as patientweight or known insulin sensitivity, or it may be defined as aconservative value based on a worse case physiological model of apatient (that is, the most insulin sensitive). For a subsequenttitration, the insulin titration sensitivity (ITS) may be estimated asthe change-in-median-glucose/change-in-insulin. The insulin changerecommendation for this titration could then be calculated as MTT/ITS;however, it would be safer, since the ITS is an estimate, to reduce theinsulin change by taking into account the uncertainty in the ITSestimate.

Estimating a Varying ITS Value Over Time

Alternatively, the ITS value can be refined over time based on pastpatient data and a priori population information. Let median glucosemeasurement Gm(k) be computed and stored at every titration period indexk. Let the insulin dose I(k) be stored at every titration period indexk. Let the latest ITS value γ(k) be a function of Gm and I at the latestand previous titration period indices k and k−1:

$\begin{matrix}{{\gamma(k)}\text{:=}\frac{{{Gm}(k)} - {{Gm}\left( {k - 1} \right)}}{{I\left( {k - 1} \right)} - {I(k)}}} & \lbrack 1\rbrack\end{matrix}$

Then, for the next titration period index k+1, the recommended insulindose I(k+1) is equal to the latest dose I(k) plus an adjustment factor:

$\begin{matrix}{{I\left( {k + 1} \right)} = {{I(k)} + \frac{{{Gm}(k)} - {{Gm}\left( {k + 1} \right)}}{\gamma\left( {k + 1} \right)}}} & \lbrack 2\rbrack\end{matrix}$

Note that in Eqn. 2, the ITS value γ(k+1) for the next titration periodis not directly known, hence an estimate, {circumflex over (γ)}(k+1),must be made. The estimation of γ(k+1) is deferred after other elementsof the recommended insulin dose, I(k+1), for the next titration periodhas been determined.

Let the MTT value for the next titration period be represented byδ(k+1), as computed by the glucose control chart-based strategy definedabove and illustrated in FIG. 36. Then, we would like for the nextmedian glucose Gm (k+1) to change from the latest median glucose Gm(k)in the amount specified by δ(k+1). The target median for the nexttitration period, Gt(k+1), is then:

Gt(k+1):=Gm(k)−δ(k+1)  [3]

Setting the next Gm value (i.e. Gm(k+1)) to equal the MTT-derived targetvalue Gt, and substituting the ITS value for the next titration periodwith its estimate {circumflex over (γ)}(k+1) (yet to be defined), onecan compute the next titration dose I(k+1):

$\begin{matrix}{{I\left( {k + 1} \right)} = {{{I(k)} + \frac{{{Gm}(k)} - {{Gt}\left( {k + 1} \right)}}{\hat{\gamma}\left( {k + 1} \right)}} = {{I(k)} + \frac{\delta\left( {k + 1} \right)}{\hat{\gamma}\left( {k + 1} \right)}}}} & \lbrack 4\rbrack \\{i.e.} & \; \\{{{I\left( {k + 1} \right)} = {{I(k)} + {\Delta\left( {k + 1} \right)}}},{{\Delta\left( {k + 1} \right)} = \frac{\delta\left( {k + 1} \right)}{\hat{\gamma}\left( {k + 1} \right)}}} & \;\end{matrix}$

Estimating a Varying ITS Value Over Time Using a Moving Average of PastValues

In the second embodiment, estimation of the ITS value for the nexttitration period is obtained from the moving average of N past computedITS values:

$\begin{matrix}{{{\hat{\gamma}\left( {k + 1} \right)}:={\frac{1}{N}{\sum\limits_{j = {k - N}}^{k}{\gamma(j)}}}},{{\gamma(k)} \geq 0}} & \lbrack 5\rbrack\end{matrix}$

In other words, the next insulin dose is calculated by using Eqn. 4,where the next ITS value is determined by Eqn. 5, and the next MTT isdetermined by the control grid.

Estimating a Varying ITS Value Over Time Using a One-Step PredictionBased on Past Values

In the second third embodiment, the estimation of the ITS value for thenext titration period is obtained from the projected straight lineLeast-Squares (LS) Error fit of N past computed ITS values (FIG. 37). Inother words, the next insulin dose is calculated by using Eqn. 4, wherethe next ITS value is determined by the projected LS error fit asillustrated in FIG. 37, and the next MTT is determined by the controlgrid.

Using MTT to Provide Fixed Step Dose Changes

In the third fourth embodiment, the ITS value and MTT are used toprovide a fixed set of increments depending on the HCP's assessment ofthe patient's ITS value, combined with the MTT sign (i.e. positive ornegative) is used to determine a fixed amount. The result is aprogression of titration changes that are more similar to currentMD-based consensus guidelines. For example:

I(k+1)=I(k)+Δ(k+1)  [6]

Where, if the HCP deems the patient's ITS to be on the extremely lowside, A is determined by:

$\begin{matrix}{{\Delta\left( {k + 1} \right)} = \left\{ \begin{matrix}{{+ 1}U} & {{{if}\mspace{14mu}{\delta\left( {k + 1} \right)}} > {20\mspace{14mu}{{mg}/{dL}}}} \\{{- 1}U} & {{{if}\mspace{14mu}{\delta\left( {k + 1} \right)}} < {{- 10}\mspace{14mu}{{mg}/{dL}}}} \\0 & {otherwise}\end{matrix} \right.} & \lbrack 7\rbrack\end{matrix}$

Note that the decision to choose the values +1 and −1 units, as well asat least a 20 mg/dL MTT for a dose increase and a −10 mg/dL MTT for adose decrease depends on the HCP's expertise. The determination ofwhether a patient's ITS (which can be computed by either the movingaverage of N past values, projected LS error fit of the N past values,etc.) is on par with the population average or not primarily depends onthe HCP's assessment of the patients insulin sensitivity factor and thepatient's past propensity for observed or symptomatic hypoglycemia.Alternatively, these values can also be set a priori based on populationstudy data. A mechanism similar to the ADA/EASD consensus guideline canalso be adopted:

$\begin{matrix}{{{I\left( {k + 1} \right)} = {{I(k)} + {\Delta\left( {k + 1} \right)}}}\mspace{405mu}} & \lbrack 8\rbrack \\{{\Delta\left( {k + 1} \right)} = \left\{ \begin{matrix}{{+ 4}U} & {{{if}\mspace{14mu} 50} < {{\delta\left( {k + 1} \right)}{{mg}/{dL}}}} \\{{+ 2}U} & {{{if}\mspace{14mu} 0} < {\delta\left( {k + 1} \right)} \leq {50\mspace{14mu}{{mg}/{dL}}}} \\0 & {{{if}\mspace{14mu}{\delta\left( {k + 1} \right)}} = {0\mspace{14mu}{{mg}/{dL}}}} \\{\min\left( {{{- 4}U},{{- 0.1}{I(k)}}} \right)} & {otherwise}\end{matrix} \right.} & \lbrack 9\rbrack\end{matrix}$

The primary difference between the embodiment described in Eqns. 8 and 9and that of the ADA/EASD consensus guideline is that in the consensusguideline, the MTT is based solely on the median glucose, and takes noconsideration of the risk of hypoglycemia due to the patient's glycemicvariability. For comparison, the consensus guideline is charted into thecontrol grid (FIG. 38), where the single hypo risk line has beenreplaced by the upper and lower target glucose ranges at 130 (greendash-dot line) and 70 (red dash-dot line) mg/dL values, and the use ofthe 95% confidence has been replaced by either a measurement average orany one measurement below 70 mg/dL. Whenever Gm is within the 2 targetlimits, δ is assumed to be 0.

Providing an Adaptive Safety Factor when Increasing Dose

In the fifth embodiment, an additional safety element based on thevariability of ITS over many periods are used to reduce the chance of anexcessive dose increase. The safety element involves adding amultiplicative safety factor α, which is varied over time to accommodatefor the patient's changing situation. A preferred embodiment of thissafety element modifies Eqn. 4 as follows:

$\begin{matrix}{{I\left( {k + 1} \right)} = {{I(k)} + {\Delta\left( {k + 1} \right)}}} & \lbrack 10\rbrack \\{{\Delta\left( {k + 1} \right)} = \left\{ \begin{matrix}{{\alpha\left( {k + 1} \right)}\frac{\delta\left( {k + 1} \right)}{\hat{\gamma}\left( {k + 1} \right)}} & {{{if}\mspace{14mu}{\delta\left( {k + 1} \right)}} > 0} \\\frac{\delta\left( {k + 1} \right)}{\hat{\gamma}\left( {k + 1} \right)} & {otherwise}\end{matrix} \right.} & \;\end{matrix}$

Note that the safety factor only affects dose increase, in the sensethat large ITS variability reduces the certainty of the information,which may increase the risk of unmodeled hypoglycemia. As a result, whatwas deemed to be a safe dose increase may need to be slightlyattenuated. The safety factor can start at a neutral value of 1, whichmakes both possibilities described in Eqn. 10 above identical. In thepreferred embodiment, the safety factor is computed relative to an apriori baseline ITS variability vb:

$\begin{matrix}{{\alpha\left( {k + 1} \right)} = \frac{1}{1 + \left\lbrack \frac{v(k)}{vb} \right\rbrack^{2}}} & \lbrack 11\rbrack\end{matrix}$

Where the latest variability v(k) is computed from the standarddeviation of the past N ITS values relative to the best fit line asdescribed in FIG. 37. Alternately, the baseline ITS variability can becomputed online based on ITS values over a longer horizon N2 (N2>>N).

Accounting for Changes in Glucose Variability Between Titrations

In the five embodiments described, the determination of the Margin ToTreat (MTT) value for the next titration period index k+1, δ(k+1), iscomputed with the assumption that the “certainty bubble” is wide enoughto account for slight changes in the patient's glucose variability Gvbetween the latest and next titration periods. In other words, anychanges in basal insulin dose will only affect Gm, and treatmentuncertainties due to changes in Gv is accounted for by the “certaintybubble”. The following 3 embodiments account for changes in Gv whendetermining the next basa insulin dose I(k+1).

Accounting for Changes in Glucose Variability Between Titrations Via aSimple Decoupled Model

In a sixth embodiment, the progression of the patient's glucosevariability Gv is tracked in order to estimate the amount of change inglucose variability. This process can be similar to the estimation/onestep projection of ITS as depicted in FIG. 37. The difference is insteadof using past ITS to predict the next ITS value, the fifth embodimentuses past Gv to predict the next Gv. This information can then be usedto adjust the MTT, by replacing Gv(k) of the latest “certainty bubble”with the predicted Gv(k+1) value, thereby shifting the patient's glucosecontrol value to the left or right based on the difference in Gv.Afterwards, basal insulin is assumed to only affect Gm. Hence, MTT andthe rest of the calculation can be performed as before.

Accounting for Changes in Glucose Variability Between Titrations Via aCoupled Insulin Sensitivity Gradient Model

In a seventh embodiment, the adjustment of MTT value to account forglucose variability relies on the estimation of an insulin titrationgradient (ITG), Γ, finding a lowest point in the “certainty bubble” fromthe latest titration index k from a possibility of points whose tangentline is parallel to the Hypo risk line, and finally calculate therecommended insulin dose I for the next titration index. The details areoutlined as follows.

In the foundation of the prior embodiments, it was assumed that a changein basal insulin I affects the patient's median glucose Gm, but notglucose variability Gv. As a result, the notion of insulin titrationsensitivity relates changes in I to changes in Gm. In a morecomprehensive model, this one-dimensional concept is replaced by aninsulin titration gradient, where the vertical component is identical tothe definition of ITS, and the horizontal component relates changes in Ito changes in Gv. In other words:

$\begin{matrix}{{\Gamma(k)}:={{{\frac{{{Gv}(k)} - {{Gv}\left( {k - 1} \right)}}{{I\left( {k - 1} \right)} - {I(k)}}e_{x}} + {\frac{{{Gm}(k)} - {{Gm}\left( {k - 1} \right)}}{{I\left( {k - 1} \right)} - {I(k)}}e_{y}}} = {{{\Gamma_{x}(k)}e_{x}} + {{\Gamma_{y}(k)}e_{y}}}}} & \lbrack 12\rbrack\end{matrix}$

The effect of basal insulin I is now a vector that spans the horizontalbasis e_(x) representing glucose variability, and the vertical basise_(y) representing median glucose. Both e_(x) and e_(y) are unityvectors. This vector adaptation to the basic principle for the fourexample embodiments illustrated in FIG. 36 is illustrated in FIG. 39.

Take candidate points in the “certainty bubble” whose tangent (reddotted line) parallels the Hypo risk line, and pick one with thesmallest median glucose value. For the moment, assume I(k+1), the valueof ITG for the next titration index, has been estimated, and has thedirection as depicted by the red arrow in FIG. 39. Then, extend thepreviously selected point towards the Hypo risk line along the directionof I(k+1). The vertical component is similar to the original MTT asdescribed in the first four embodiments. However, the value of MTT(represented by the distance δ in FIG. 39) is somewhat shorter than thesimilar situation depicted in FIG. 36. The reason is that in the vectorcase, any non-vertical effect of changing insulin dose can reduce orincrease the likelihood to crossing the Hypo risk line.

Similar to the original scalar case (i.e. the first five embodiments),the recommended insulin dose I(k+1) equals the latest dose I(A) plus anadjustment factor:

$\begin{matrix}{{I\left( {k + 1} \right)} = {{I(k)} + \frac{{{Gm}(k)} - {{Gm}\left( {k + 1} \right)}}{{\hat{\Gamma}}_{y}\left( {k + 1} \right)}}} & \lbrack 13\rbrack\end{matrix}$

Where {circumflex over (Γ)}_(y) (k+1) is the estimate for the verticalcomponent of ITG, to be defined. The target median glucose depends onthe latest median glucose Gm(k) and the MTT represented by the length δin FIG. 39:

Gt(k+1):=Gm(k)−δ(k+1)  [14]

Then, setting the next Gm value (i.e. Gm(k+1)) to equal the MTT-derivedtarget value Gt, and substituting Γ_(y)(k+1) value for the nexttitration period with its estimate {circumflex over (Γ)}_(y) (k+1) (yetto be defined), one can compute the next titration dose I(k+1):

$\begin{matrix}{{{I\left( {k + 1} \right)} = {{I(k)} + {\Delta\left( {k + 1} \right)}}},{{\Delta\left( {k + 1} \right)} = \frac{\delta\left( {k + 1} \right)}{{\hat{\Gamma}}_{y}\left( {k + 1} \right)}}} & \lbrack 15\rbrack\end{matrix}$

In this embodiment, estimation of the vertical component of the ITGvalue for the next titration period is obtained from the moving averageof vertical components of N past ITG values:

$\begin{matrix}{{{{\hat{\Gamma}}_{y}\left( {k + 1} \right)}:={\frac{1}{N}{\sum\limits_{j = {k - N}}^{k}{\Gamma_{y}(j)}}}},{{\Gamma_{y}(k)} \geq 0}} & \lbrack 16\rbrack\end{matrix}$

The horizontal component of the ITG value could also be independentlycomputed in a similar manner:

$\begin{matrix}{{{{\hat{\Gamma}}_{x}\left( {k + 1} \right)}:={\frac{1}{N}{\sum\limits_{j = {k - N}}^{k}{\Gamma_{x}(j)}}}},{{\Gamma_{x}(k)} \geq 0}} & \lbrack 17\rbrack\end{matrix}$

Accounting for changes in glucose variability between titrations via acoupled insulin sensitivity gradient model, with a coupled estimation ofthe insulin sensitivity gradient model.

In an eighth embodiment, the process is identical to the seventhembodiment, with the exception of a joint estimation of the vertical andhorizontal components of the ITG value. A preferred implementation is todefine a polar representation of ITG:

$\begin{matrix}{{{\Gamma_{m}(k)}:=\sqrt{\left\lbrack {\Gamma_{x}(k)} \right\rbrack^{2} + \left\lbrack {\Gamma_{y}(k)} \right\rbrack^{2}}}{{\Gamma_{r}(k)}:=\frac{\Gamma_{x}(k)}{\Gamma_{y}(k)}}} & \lbrack 18\rbrack\end{matrix}$

Where the ratio is selected such that singularity is avoided by notplacing the typically smaller element, Γ_(x), on the denominator.Following this polar representation, one-step predictions of themagnitude Γ_(m) and ratio Γ_(r) can be conducted by independentlyapplying the same LS error fit of a line depicted in FIG. 3, replacingthe ITS by Γ_(m) and Γ_(r) in each case, independently.

Variability: The system would notify the patient and/or HCP whenvariability was too high and needed to be reduced if they wanted toachieve a lower target medium. Specifically, the system would output: a)lowest median achievable for current variability, and b) variabilitytarget needed to achieve median target. The variability would beindicated as too high if the MTT was greater than the difference betweenthe current TRP and the desired median. This output would mostbeneficially be made to the patient's HCP so they could work with thepatient to address self-care behavior to address variability. A targetvariability could also be provided, which in one embodiment may becalculated as the intersection of the hypo risk line and the targetmedian.

Using methods described in any of the embodiments, the lowest medianachievable Ĝm(k+1) given the current variability is equal to the targetmedian glucose used in the calculations, as outlined in Eqns. 3 and 14:

Ĝm(k+1):=Gt(k+1)  [19]

The variability target Ĝv(k+1) needed to achieve median target can becomputed in the seventh and eighth embodiments by taking I(k+1) (thesuggested insulin dose), {circumflex over (Γ)}_(x) (k+1) (the estimatedhorizontal component of the ITG), I(k) (the latest measured insulindose), and Gv(k) (the latest glucose variability), in the followingmanner:

Ĝv(k+1)=Gv(k)+└{circumflex over (Γ)}_(x)(k+1)[I(k)−I(k+1)]┘  [20]

Basal vs. basal/bolus: The algorithm described thus far could apply toglucose lowering medications and specifically medications that can causehypoglycemia, such as basal insulin and sulfonyurea. Extending thisalgorithm to basal and prandial dose insulin is more complicated, asthere are four key dosing times-per-day, each associated with a glucoseprofile time-per-day: fasting, post-breakfast, post-lunch andpost-dinner (note that other dosing regimens may be considered here moregenerally). A control grid would be determined for each period. Oneapproach for this more complicated dosing is to incorporate abasal/bolus interaction model into the titration algorithm. Abasal/bolus interaction model would take into account that the basaldose impacts not only fasting glucose but also post-meal glucose, andthat the rapid-acting dinner dose often impacts the fasting glucose. Asimple interaction model could associate the long-acting dose with anapportioned effect on each of the four time-of-day median glucoselevels: for instance, an incremental insulin sensitivity could bedetermined for each time-of-day period associated with the basal dose(mg/dL per unit). Likewise, the dinner and fasting periods could have inincremental insulin sensitivity associated with the rapid-acting dinnerdose.

Meter vs. PC Software vs Hybrid: This system can be implemented anynumber of ways. For instance, it may be implemented fully on a glucosemeter. It is likely that the meter would have features that allowed anHCP or other caregiver to configure the titration parameters, such asmaximum titration levels or other possible parameters for the algorithm.The algorithm could also be implemented fully on a personal computer orother non-meter computing device or remotely in the “cloud”. Here theHCP may be involved to approve the titration recommendations. A hybridsystem can be contemplated where portions of the algorithm are availableeither on the meter and/or on the remote computing device. One exampleof this embodiment is where the algorithm is implemented remotely, andan HCP is notified of the titration recommendation and must approve it.The remote computer would then download the titration recommendation.Many other variations of this hybrid scheme can be contemplated.

Simplified Expert Algorithm for Therapy Management System InformaticsSystem

An embodiment related to a Therapy Management System (TMS) is disclosed.In the prior art, an “Expert Algorithm” calculates the numerical resultsthat drive the treatment recommendations. The prior art algorithm isnumerically complex and takes a significant amount of time to produceresults. The waiting time is noticeable and detracts from the user'sexperience.

In particular, the calculation of the uncertainty in the estimate takesa substantial amount of computation time. The uncertainty is used tofind the distance from the center value to a value called the “TreatmentRecommendation Point” (TRP), which is vital to the operation of the TMS.Twice this distance is known as the “Figure of Merit” (FOM). Thisembodiment uses the results of the current expert algorithm to make asimple approximation to the FOM, thus bypassing a lengthy calculation.

Referring to the drawings, FIG. 40 shows a scatter plot of the figure ofmerit vs. the South40 and the glucose median. The scatter plot hasstrong vertical stripes which indicates that the FOM is a strongfunction of the south40 and the FOM is independent of the median. FIG.41 presents a FOM/2 vs. the normalized South40. The normalization is1/the square root of the sample size. A very strong correlation isapparent. The dashed line is the x=y line. The second line is the bestfit and is slope=0.8745 and intercept=2.57 mg/dL. FIG. 42 presents theerror using the FOM/2−S40/square root of sample size. FIG. 43 presentsthe error using a least squares fit. FIG. 44 shows the error with theleast squares fit histogram. By using the least squares fit, there areno errors larger than 5 mg/dL and the errors are symmetrical about 0.

As shown in FIGS. 40 through 47, there is a very strong linearrelationship between the FOM, and the glucose variability (the“South40”) and the number of data points, N. Using a linear regression,we find that

FOM/2=2.57+0.8745*South40/√N,

Thus, 5-10 seconds of calculation can be replaced by a few arithmeticoperations. This relationship was tested on a different set of data (theTest Set). The error between the calculated FOM/2 and the approximatedFOM/2 rarely exceeds 5 m/dL, which is much smaller than the FOM itself.

FIGS. 45 through 47 are based on test set data and FIG. 45 shows theerror using the FOM/2=S40/square root of the sample size. FIG. 46 showsthe error using the least squares fit, and the graph of FIG. 47 showsthe error with the least squares fit histogram. The test set indicatesthat the correlation persists, and that the error is small.

Use of Meal Markers and Glucose Pattern Analysis to Improve TreatmentRecommendations

Using meal markers and/or glucose patterns to drive patient treatmentrecommendations. Previous recommendation systems do not use mealinformation and also do not exploit temporal relationships betweenglucose values. Use of meal markers can be used to give more targeted(and thus better) recommendations. Pattern analysis using temporalrelationships opens up new analysis results that would have been missedwhen only looking at the collected data in aggregated form.

Meal markers could be used in many ways. Using them to bin data (e.g.post-Lunch values are those values after a recorded meal event between10 am and 3 pm) would allow more accurate differentiation of points intomeal ‘bins’, leading to more accurate recommendations for meal-specificproblems. Recorded meal size and timing relative to insulin usage can beused to evaluate how well the patient is currently managing meals andsuggest either overall (i.e. all meals) or targeted (i.e. only somemeals) behavior improvements to improve glycemic control/reducevariability. Meal marker information can be collected in a variety ofways:

Explicitly (i.e. there is UI to allow the user to record a meal);

Implicitly based on other data collected (e.g. insulin informationentered manually or read automatically from a dosing device); and

Triggered based on an event (e.g. a configurable amount of time after aglucose measurement is taken).

The meal information could also be used to drive reminders for the user(e.g. to warn the user that they may have missed a meal bolus). Thislogic could take a variety of forms:

Warn if there is a large rise without a recorded bolus

Warn if no meal/insulin is logged for a long amount of time

Patterns such as rapid rises and falls can also be fed into theanalysis. Rapid falls could be treated as an independent risk factor forhypoglycemia. Recommendations could be adjusted accordingly in theirpresence. Rapid rises can be treated as an accurate measure of intradayvariability. The number or magnitude of rapid rises present can becompared to the overall variability to determine interday vs. intradayvariability. Having separate measures for the different classes ofvariability would allow recommendations to be targeted more precisely tothe root cause of the high variability experienced by the patient.

For example, if a high number of rapid rises or falls have beendetected, treatment recommendations may be focused on the timing andsize of boluses. In the opposite case, where it is found that there ismuch more interday variability than intraday variability, treatmentrecommendations may be focused on lifestyle changes such as meal timingand exercise.

Two Threshold Analog to Control Grid Used for Therapy Decision Support

The use of alternate metrics for a control grid used for diabetestherapy decision support is described. Also, a means to determine an“upper” threshold of a target glucose range that is equivalent to amedian target defined by A1c is described.

In prior control grids, the upper range was set by doctor preference andcustom, for instance 190 mg/dL. However, for patients with high medianor high variability, this traditional upper limit may not beappropriate. A means to set this range based on target A1c (or glucosemedian), measured variability, and hypo risk tolerance is described.

The control grid, disclosed previously, is the key part of a method thatcan be used to generate diabetes therapy recommendations from glucosedata. The control grid is a plot of a measure of central tendency (e.g.,median) vs. a measure of variability (e.g., median minus the 10% ile).The glucose data are used to generate these measures. Zones can bedefined on the control grid corresponding to treatment recommendations,such as “safe to increase dose” or “reduce variability before increasingdose”. In this way, glucose data can be mapped to recommendations.

Zones can be defined by identifying boundaries, such as above or belowthe target median, and above or below a metric that defines highhypoglycemia risk. A novel aspect of this method is a boundary thatidentifies high and low variability zones. One particularly usefulvariability boundary identifies the maximum variability that can coexistwith low hypoglycemia risk and while below the target median.

In clinical practice, identifying high and low variability in this wayis useful because it allows notification to a clinician that whenglucose variability is high, adjusting medication dose amounts alone maynot be useful for improving glycemic control, but that steps must betaken to identify causes of high variability and to mitigate these.

The disclosure below describes alternative methods to achieve theglucose-to-recommendation mapping. For example, instead of using medianand variability, the method could employ any two statistical measuresthat define a distribution of data. For instance, the statisticalmeasures could be based on a glucose target range. A target range,commonly used in diabetes management and well understood by diabetesclinicians, is simply a glucose range defined between two thresholds—forinstance, G_(LOW)=70 mg/dL and G_(HIGH)=140 mg/dL. For continuous sensordata, a common measure related to target range is “time-within-target”(t_(WT)), defined as the percentage of data within this range or theaverage number of hours in a day within this range. Similarly, the“time-above-target” (t_(AT)) and “time-below-target” (t_(BT)) aredefined.

If we consider that glucose data can be modeled as a distribution (forinstance, a gamma distribution), for predefined target thresholdsG_(LOW) and G_(HIGH) we can calculate t_(BT) and t_(AT). An example ofthis is shown in FIG. 48.

We can also define, for these same thresholds, a metric t_(BT_HYPO) inwhich if exceeded by t_(BT), then the patient may be determined to be athigh hypoglycemia risk. For instance, we may define high hypoglycemiarisk as whenever t_(BT) is greater than 5% for G_(LOW)=70 mg/dL; in thisexample, t_(BT_HYPO)=5%. Likewise, a metric t_(AT_HYPER) can be definedin which if exceeded by t_(AT), then the patient may be determined to beat high hyperglycemia risk. The degree of hypoglycemia risk can beadjusted by adjusting either the G_(LOW) or t_(BT_HYPO). Likewise forhyperglycemia risk and G_(HIGH) or t_(A_HYPER).

Any two of these three measures, t_(BT), t_(AT) and t_(WT), can be usedto define a control grid. FIGS. 49A-49F show a control grid defined bytAT and tBT. In FIG. 49A, a boundary is defined by t_(BT_HYPO), and twozones are created that identify hypoglycemia risk. FIG. 49B shows adifferent boundary defined by t_(AT_HYPER), which creates two zones thatidentify hyperglycemia risk. Alternatively, it may be more clinicallyappropriate to define a hyperglycemia risk boundary as a constant valueof t_(WT), which would translate to the t_(AT) vs t_(BT) grid as a line,illustrated in FIG. 49C. In FIG. 49D, we show an example of how theseboundaries can be combined. In this case, the hypo risk boundary hasprecedence (since treating hypoglycemia is typically prioritized overtreating hyperglycemia in practice). This results in three zones, asshown.

An important feature of the control grid, disclosed previously, is theidentification of variability and its relationship to hypoglycemia risk.An appropriate boundary definition for high variability is where nofurther increase in variability can coexist with the target region. Thisconstant variability boundary is illustrated in FIG. 49E, overlaying theother boundaries. In FIG. 49F, the boundaries are shown to identify fivedifferent zones; these zones can be mapped to assessments of thepatient's glycemic control as shown in the table below, or to clinicalrecommendations, or to other classifications.

Z1 = In target Z2 = Safe-to-treat Z3 = Safe-to-treat, reduce variabilityZ4 = High hypoglycemia risk, reduce variability Z5 = High hypoglycemiarisk, reduce medication

As mentioned, any statistical measure can be similarly used on thecontrol grid. FIG. 50 shows a grid with zones identified with t_(WT) onthe y-axis and t_(BT) on the x-axis.

An alternate statistical measure that could be used, similar to thosealready discussed, is percentiles. For instance, the y-axis could berepresented by the glucose 75%-ile (G₇₅) and the x-axis represented bythe glucose 10%-ile (G₁₀). Now for predetermined hypo and hyper riskboundaries defined by G_(LOW) and t_(BT_HYPO), and G_(HIGH) andt_(AT_HYPER), we can determine equivalent target thresholds in terms ofthe G₁₀ and G₇₅ measures, G_(LOW10) and G_(HIGH75). This control grid isshown in FIGS. 51A-51B.

As an example of the above embodiment, assume that hypo risk is definedby G_(LOW)=70 mg/dL and t_(BT_HYPO)=7.2%, which means that a patient isat high risk for hypoglycemia if their glucose data are less than 70mg/dL for more than 7.2% of the time. Also, assume that hyper risk isdefined by G_(HIGH)=200 mg/dL and Tb=10%, which means that a patient isabove target if their glucose data are greater than 200 mg/dL for morethan 10% of the time. Using these values as parameters that define agamma distribution model, we can calculate the target thresholds interms of the measured percentiles, G₁₀ and G₇₅. G_(LOW10)=76 mg/dL andG_(HIGH75)=163 mg/dL. Patients and clinicians can now use these targetsto control their glucose in an intuitive and robust way.

Note that any desired percentile may be used as a measure. Some choiceswill be more practical than others. For instance, it may not bepractical to use G₁₀ and G₁₁, since it would require a tremendous amountof data to resolve the 1% difference. Also, G₂ and G₉₈ may not be goodchoices do to the large amount of data required to resolve thedistribution tails. The original disclosure of the control grid choseG₅₀ and (G₅₀-G₁₀) as measures, since this roughly corresponds tochoosing a central tendency measure and a variability measure, which isa common way to define distributions and requires less data toaccurately estimate. G₇₅ and G₁₀, described above, may be good choices,as would be G₉₀ and G₁₀, as they lead to definition of a target rangewhich is well understood by patients and clinicians as a tool fordiabetes management. Also, these percentiles correspond to those used inthe Ambulatory Glucose Profile standard used by many diabetes cliniciansto assess patient glucose data. Finally, a good choice for percentilesmay be G₇₅ and (G₅₀-G₁₀) or G₉₀ and (G₅₀-G₁₀), as these provide a hightarget range limit, and a variability measure that is well estimated bysmall amounts of data and is readily understood to be primarily anestimate of variability.

In previous control grid disclosures, it was described how the hypo riskboundary can be defined from glucose data from a population of patients.In this case the boundary was determined in terms of the G₅₀ and(G₅₀-G₁₀) measures. Using the same method, the boundary may be chosenfor any percentile measures.

In general, this method could use any two statistical measures of data.Examples of other statistical measures are mean, mode, standarddeviation, variance, MARD, LGBI, etc.

For the threshold that defines the high end of the target range, forinstance G_(HIGH75) or G_(HIGH90), it can be useful to determine thesethresholds based on the desired target A1c. In previous control griddisclosures, it was described how the target zone on the control gridhad an upper bound defined as the target glucose median. This is thesame as G_(HIGH50). It has also been described how this target mediancan be associated with a target A1c. This is a useful parameter that canbe adjusted by the clinician as it allows them to set an A1c target thatmay be more reasonable for the patient. For instance, the ultimate goalfor all people with diabetes is to maintain an A1c value below 7%.However, if a patient currently as an A1c of 11%, it may be discouragingto use the 7% goal—it may be more realistic for the clinician to set anachievable goal of 9.5%. When the patient reaches this goal, theclinician can set a lower goal, and so forth. The target glucose medianG_(HIGH50) can be determined from the target A1c selected, as describedelsewhere.

If a percentile measure different from the median is used, however, theequivalent target threshold must be determined in terms of thisdifferent measure. Unfortunately, unlike the median target which can bedefined as described above for data with any possible value ofstatistical variability, other measures for the target, such as the75%-ile and the 10%-ile, will depend on data variability. That is, usingthe measure G₇₅ for example, for a given median target, a correspondingG_(HIGH75) could be calculated given an assumed distribution with amedian at G_(HIGH50) and a defined variation metric. This variationmetric could be defined to be any value, but a logical choice is for itto correspond to a distribution that exactly meets the hypo riskcriteria. Therefore, G_(HIGH75) can be determined from a gammadistribution defined by a median at G_(HIGH50), and with variationdefined by G_(LOW) and t_(BT_HYPO). As an example, given G_(LOW)=70mg/dL and t_(BT_HYPO)=7.2%, and G_(HIGH50)=154 mg/dL, the gammadistribution with these parameters would result in a value at the 75%percentile of G_(HIGH75)=209 mg/dL. This example is illustrated in FIGS.52A and 52B where the G₅₀ vs (G₅₀-G₁₀) control grid is shown with anequivalent G75 vs (G50-G10) control grid.

The key benefit of determining an equivalent target at the 75% or the90% percentiles is that it is more natural for patients to manage theirglucose based on keeping their glucose below a high target, rather thantrying to achieve a median or mean glucose target. For example, a goalcould be to keep their glucose below a value of 175 mg/dL 90% of thetime. This target glucose value, G_(HIGH90), that is determined by aclinician who defines their target A1c and their hypo risk level, can beprovided to the clinician, caregiver and/or patient. One use would be todisplay a line on the AGP at this threshold value. Another use would beto load this value into a glucose meter which would be programmed todisplay the value as a line on glucose history plots, or to use thevalue in a calculation of t_(AT).

Alternatives to the methodologies described above can be contemplated bychanging the role of “inputs” and “outputs”. For instance, rather thandetermine a high range threshold by setting the A1c goal and hypo riskthreshold, a system could allow the clinician to set the desire highrange threshold and hypo risk threshold, and determine the median targetor equivalent A1c value. Another example is a system that would allowthe clinical to set the A1c goal, hypo risk threshold and desired highrange threshold percentile measure, and determine the associated highrange threshold. Many other possibilities along this line are obvious.Other possible systems include one where a routine is applied to thedata to determine the best fit to one of a number of possibledistribution models, and this selected distribution is used in themethod described.

When using sensor data, it is likely that enough data are collected toprovide a good estimate of a distribution. For SMBG data, where fewerdata points are usually available, it may be useful to fit the data to adistribution model, such as a gamma distribution, to calculate t_(BT)and t_(AT).

Note that distributions other than a gamma distribution may be used;however, for most common distributions, two parameters must be definedin order to uniquely define the distribution. This is the case for theexamples above. The method described here could be generalized todistributions that require three or more parameters to uniquely define;in these cases, 3 or more measures need to be used, with correspondingmetrics defined.

Another embodiment of the method described would incorporate two or moreboundaries associated with a measure; for example, instead of this“above” and “below” (or “high” or “low”), we may have two boundariesthat “high”, “moderate” or “low.” See FIGS. 53A and 53B. Previousdisclosures describe these boundaries defined by gradations in theuncertainty associated with a measure; however, multiple boundaries maybe associated with any measure for any reason.

Rule-Based Mapping to Medication Adjustment Guidance

A means of providing diabetes treatment recommendations using rule-basedlogic applied to a sample of glucose measurements is disclosed. Priormethods for recommendation logic have not typically incorporated glucosevariability. The current invention leverages the observed andclinically-relevant relationship between median glucose, low-rangeglucose variability and hypoglycemia risk to derive treatmentrecommendations. This potential advantage is to make the recommendationsmore applicable and useful to patients and HCPs.

The current embodiment provides a means of guiding diabetes treatmentintervention by rule-based decisions applied to a sample of glucosemeasurements. As background, prior methods are based on determining acentral-tendency value (i.e. 50th percentile) and a low-rangevariability value (i.e. 50th-10th percentile) to characterize thehyperglycemia control, variability control, and future risk ofhypoglycemia. Given thresholds to determine categories of control (i.e.“Above”, “Within”, “Below”, or “High”, “Moderate”, “Low”) for each ofthese, a number of zones can be associated with outputs to providediabetes treatment guidance. An example of these zones are shown in FIG.54. This defines the main example of a “zone assignment” algorithm.

The current invention would use a number of rule-based decisions toarrive at “control zones”, which could follow the same or similarmapping to guidance output, such as that shown in FIGS. 55A and 55B.These rules could bypass estimating glucose variability directly, butrather infer the glucose variability based on the outcomes of the rulechecks. For example, in a simple case of checking above and below asingle target glucose range for a single time period of interest (say“overnight”), the zone-assignment algorithm could be to check if therate of above-target-range glucose values exceed a threshold or not(therefore “hyperglycemia control” would be “Above” or “Within”) andthen check if the rate of below-target-range glucose values exceed athreshold or not (therefore “hypoglycemia risk” would be “Low or“High”). The glucose variability could be inferred (as listed in Table5) and the guidance output determined by the control zone as listed inTable 6.

For instance, if below both the hypo and hyper risk thresholds, then thevariability would be determined to be low. Alternatively, if above boththe hypo and hyper risk thresholds, then the variability would bedetermined to be high. If above the hyper risk threshold but below thehypo risk threshold, or if below the hyper risk threshold but above thehypo risk threshold, the variability may not be discernible using thismethod; however, in this case the recommendations may be limited tomedication adjustment only, or may always include guidance to reducevariability as a conservative approach.

TABLE 5 Example rule-based control zone assignment algorithmHyperglycemia Within Target Within Target Above Target Above Targetcontrol (<20% above (<20% above (>=20% (>=20% (example high threshold)high threshold) above high above high criteria) threshold) threshold)Hypoglycemia Low Risk High Risk Low Risk High Risk Risk (<10% below(>=10% below (<10% below (>=10% below (example low threshold) lowthreshold) low threshold) low threshold) criteria) Glucose Low Low LowHigh Variability Variability Variability Variability Variability(inferred) Control Zone 1 10 3 12

Another method would be to explicitly estimate variability and compareto a threshold (or multiple thresholds) to determine if the result is“high” variability or “low” variability. Then the hypo and hyper riskmeasures are determined and applied to a risk metric table; one table isused when “high” variability is detected and another table is used if“low” variability is detected. Note that tables may be used or functionsor any other equivalent means to process these results.

Another method would be to generate new thresholds to determine “high”or “low” variability, if above the hyper risk threshold but below thehypo risk threshold, or if below the hyper risk threshold but above thehypo risk threshold. One embodiment would be, if above the hyper riskthreshold but below the hypo risk threshold, to determine a new highthreshold as the 80%-ile of the data (or using the percentilecorresponding to the hyper risk threshold), and determine a new lowthreshold as the new high threshold minus the difference between thehyper risk threshold and the hypo risk threshold. If the 10%-ile of thedata is below this new low threshold, then the variability will bedetermined to be “high”; otherwise, the variability will be determinedto be “low”. Likewise, if below the hyper risk threshold but above thehypo risk threshold, then determine a new low threshold as the 10%-ileof the data, and determine a new high threshold as the new low thresholdplus the difference between the hyper risk threshold and the hypo riskthreshold. The result of this method is essentially the same asexplicitly determining a variability measure of the data, but does notrequire this variability measure to be explicitly determined.

Other methods are related to the above in that they do not requireexplicit determination of variability. For instance, a high and lowthreshold pair can be created by adding or subtracting an identicaloffset to the hypo and hyper risk thresholds. Multiple threshold pairscan be created in this way, using a range of offsets. Then the 10%-ileand 80%-ile can be compared to all of these pairs and if for at leastone pair, if above both the hypo and hyper risk thresholds, then thevariability would be determined to be “high”; otherwise, the variabilitywill be determined to be “low”. This methodology can be extended to manydifferent percentiles and scaling schemes. For instance, instead ofdetermining the new high and low thresholds based on offsetting thehyper and hypo risk thresholds, other functions of the hyper and hyporisk thresholds could be contemplated. For example, a linear functionmay be used where a slope parameter that multiplies the hyper riskthreshold is greater than one, in order reduce the likelihood that“high” variability would be determined when the glucose levels aretending higher.

Multiple levels of rule checks could be designed for hyperglycemiacontrol and hypoglycemia risk. For example hypoglycemia risk could bestratified into three levels as:

-   -   Low Risk: <5% below low threshold    -   Moderate Risk: 5% to <10% below low threshold    -   High Risk: >=10% below low threshold

This method may be enhanced by more specifically identifying medicationsto be added, increased or decreased. A table of medications could beused where each medication (or medication class) is represented by a rowand attributes of the medication are represented in one or more columns.Attributes may include a relative effectiveness score by time period, sothat if specific time of day periods (typically defined by meal times)are assessed for a patient and it is indicated that increased medicationis recommended for that period, then the table could be scanned to lookfor the medication with the highest relative effectiveness for thatperiod. Other attributes in the table could include contraindications,cost, side-effects, inconvenience, etc., and compared with relevantpatient needs or exclusions.

Referring now to FIG. 56, a block diagram is presented showing anembodiment of a system 370 usable to accomplish the above. Inparticular, a CGM sensor 372 communicates sensed glucose signals to aprocessor 374. The processor is connected with an input device 376, suchas a keyboard or pointing device or both, and may include additional ordifferent input devices. A memory 378 stores data and programs to whichthe processor has access. Outputs from the execution of programs may bedisplayed 380 and/or printed 382. For example, Insights reports 69,control grids 145 and other data analyses or data presentations may bedisplayed and printed. The processor also has a communicationscapability 384 and may communicate over various means with otherprocessors, servers 386, as required. In regard to an HCP 388, theprocessor 374 may communicate directly 390 or the HCP may obtain theprocessor output through a server 386 in a more indirect manner.Programs, such as those disclosed above, may reside on the memory 378 ormay be obtained by the processor 374 from a remote source, such as froma memory 392 at the remote server 386. Other arrangements for data flow,application flow, and communications are possible. Multiple servers,local and remote, may participate as well as multiple memories. Althoughthe memory 378 the process or shown as a single box, it may in fact bemultiple memory devices. The same applies to other components of FIG.56. Additionally, FIG. 56 components may be disposed on many differentkinds of computing devices, such as a desktop computer, laptop computer,tablets, smart phones, and other. FIG. 56 provides only a singleembodiment.

The methodologies describe above could be combined so that the systemnot only recommends specific medications, but also indicates whenmedication adjustment may not be useful or wise, but steps to identifyand mitigate patient variability should be taken first.

Unless the context requires otherwise, throughout the specification andclaims that follow, the word “comprise” and variations thereof, such as,“comprises” and “comprising” are to be construed in an open, inclusivesense, which is as “including, but not limited to.”

While the invention has been described in connection with what ispresently considered to be the most practical and preferred embodiments,it is to be understood that the invention is not to be limited to thedisclosed embodiments and elements, but, to the contrary, is intended tocover various modifications, combinations of features, equivalentarrangements, and equivalent elements included within the spirit andscope of the appended claims.

What is claimed is:
 1. A method for determining glycemic risk of apatient during pregnancy using a continuous glucose monitor, the methodcomprising: continuously collecting glucose measurements of the patientvia a sensor of the continuous glucose monitor, wherein the glucosemeasurements are indicative of an interstitial glucose level;communicating the glucose measurements collected by the continuousglucose monitor to a processor of a computing device; computing aglucose metric based on the glucose measurements collected by thesensor; selecting a threshold for determining a hypoglycemia risk levelbased on patient specific criteria, wherein the patient specificcriteria comprises a pregnant condition of the patient; determining ahypoglycemia risk level based on a comparison of the glucose metric tothe selected threshold; and displaying on a display of the computingdevice a visual representation of the hypoglycemia risk level.
 2. Themethod of claim 1, wherein the glucose metric comprises a sum of thedifferences between the collected glucose measurements and apredetermined low glucose level divided by a total number of thecollected glucose measurements.
 3. The method of claim 1, wherein theglucose metric comprises a measure of a frequency at which the collectedglucose measurements are below a predetermined low glucose level.
 4. Themethod of claim 1, wherein the visualization comprises a colorcorresponding to the hypoglycemia risk level.
 5. The method of claim 1,wherein the visualization comprises an indication of high, moderate, orlow risk.
 6. The method of claim 1, further comprising displaying on thedisplay of the computing device a plot of the collected glucosemeasurements over time.
 7. The method of claim 1, further comprisingreceiving a selection of the threshold via an input of the computingdevice.
 8. The method of claim 1, further comprising determining thehypoglycemia risk level during a plurality of time periods in one day.9. The method of claim 1, further comprising communicating the collectedglucose measurements to a healthcare provider.
 10. The method of claim1, further comprising providing a treatment recommendation based on thecollected glucose measurements.
 11. A system for determining glycemicrisk of a patient during pregnancy, the system comprising: a continuousglucose monitor configured to collect glucose measurements of thepatient, the continuous glucose monitor comprising a glucose sensorconfigured to sense an interstitial glucose level; and a computingdevice in communication with the continuous glucose monitor, thecomputing device comprising a display, a processor, and non-volatilememory coupled with the processor, the memory storing a glucose dataprocessing program that, when executed by the processor, causes theprocessor to: determine a glucose metric based on the collected glucosemeasurements, select a threshold for determining a hypoglycemia risklevel based on patient specific criteria, wherein the patient specificcriteria comprises pregnancy, determine the hypoglycemia risk levelbased on comparison of the determined glucose metric to the selectedthreshold, and visually present the hypoglycemia risk level on thedisplay of the computing device.
 12. The system of claim 11, wherein thecomputing device comprises a smartphone.
 13. The system of claim 11,wherein the computing device comprises an input device for receiving aselection of the threshold.
 14. The system of claim 13, wherein theinput device comprises a touch screen.
 15. The system of claim 11,wherein the selection comprises a selection of a threshold from apredetermined list of thresholds.
 16. The system of claim 11, whereinthe glucose metric comprises a sum of the differences between thecollected glucose measurements and a predetermined low glucose leveldivided by a total number of the collected glucose measurements.
 17. Thesystem of claim 11, wherein the visualization comprises a colorcorresponding to the hypoglycemia risk level.
 18. The system of claim11, wherein the visualization comprises an indication of high, moderate,or low risk.
 19. The system of claim 11, wherein the computing device isconfigured to display a plot of the collected glucose measurements overtime.
 20. The system of claim 11, wherein the computing device isfurther configured to provide a treatment recommendation based on thecollected glucose measurements.